CRITICAL CURRENT DENSITIES AND N-VALUES OF MGB2 CONDUCTORS FOR SMES, MRI, AND LOW AC LOSS APPLICATIONS

Multifilamentary MgB 2 strands (filament numbers 36 to 114) prepared by the in-situ power-in-tube (PIT) route with carbon doping contents of 0, 2, and 3.2% were wound on barrels for transport J c and n -value measurement at 4.2 K in fields of up to 12 T. The strand and gauge lengths were 1 m and 0.5 m. Heat treatments at 675 °C and 650 °C centered around the melting point of Mg (650 °C) and both utilized the liquid-solid reaction. A pair of strands, with and without 2% C doping exhibited the J c (B) crossover effect. Studied were the dependencies of J c on field strength, dopant concentration, and cabling and the dependence of n -value on field strength.

Deep learning tools to accelerate antibiotic discovery

INTRODUCTION

As machine learning (ML) and artificial intelligence (AI) expand to many segments of our society, they are increasingly being used for drug discovery. Recent deep learning models offer an efficient way to explore high-dimensional data and design compounds with desired properties, including those with antibacterial activity.

AREAS COVERED

This review covers key frameworks in antibiotic discovery, highlighting physicochemical features and addressing dataset limitations. The deep learning approaches here described include discriminative models such as convolutional neural networks, recurrent neural networks, graph neural networks, and generative models like neural language models, variational autoencoders, generative adversarial networks, normalizing flow, and diffusion models. As the integration of these approaches in drug discovery continues to evolve, this review aims to provide insights into promising prospects and challenges that lie ahead in harnessing such technologies for the development of antibiotics.

EXPERT OPINION

Accurate antimicrobial prediction using deep learning faces challenges such as imbalanced data, limited datasets, experimental validation, target strains, and structure. The integration of deep generative models with bioinformatics, molecular dynamics, and data augmentation holds the potential to overcome these challenges, enhance model performance, and utlimately accelerate antimicrobial discovery.

Mechanism of enhanced critical fields and critical current densities of MgB2 wires with C/Dy2O3 co-additions

A series of monofilamentary powder-in-tube MgB2 wires were fabricated with 2 mol. % C doping and co-additions of 0-3 wt. % Dy2O3. Irreversibility fields (μ 0 Hirr ), upper critical fields (μ 0 Hc 2), and transport critical currents were measured, and from these quantities, anisotropies ( γ ) and electronic diffusivities ( D π , σ ) were estimated. The addition of 1 wt. % Dy2O3 to already optimally C-doped MgB2 wires produced higher Hc 2//ab , Hc 2//c , and Hirr values at 4.2 K. In addition, the critical current density, Jc , increased with Dy2O3 concentration up to 1 wt. % where non-barrier Jc reached 4.35 × 104 A/cm2 at 4.2 K, 10 T. At higher temperatures, for example, 20 K and 5 T, co-additions of 2 mol. % C and 2 wt. % Dy2O3 improved non-barrier Jc by 40% and 93% compared to 2 and 3 mol. % C doping, respectively. On the other hand, measurements of Tc showed that C/Dy2O3 co-additions increase interband scattering rates at a lower rate than C doping does (assuming C doping levels giving similar levels of low-T μ 0 Hc 2 increase as co-addition). Comparisons to a two-band model for μ 0 Hc 2 in MgB2 allowed us to conclude that the increases in Hc 2//ab , Hc 2//c , and Hirr (as well as concomitant increases in high-field Jc ) with Dy2O3 addition are consistent with increases primarily in intraband scattering. This suggests C/Dy2O3 co-addition to be a more promising candidate for improving non-barrier Jc of MgB2 at temperatures above 20 K.

A brief introduction to chemical proteomics for target deconvolution

OBJECTIVE

Drug-target relationships provide the basis for network-based polypharmacology, and target deconvolution is a key step in phenotypic-screening based drug discovery. Due to the complexity of the mammalian proteomics and the often-limited affinity of the lead compound, it is challenging to identify the drug targets, especially when the goal is to identify all targets. This paper attempts to provide a brief and comprehensive introduction to the various methods in chemical proteomics for target deconvolution by categorizing them into two groups: the biochemical enrichment and the proteomics-screening methods. Moreover, a brief introduction of related Mass Spectrometry techniques is also provided, together with recent progress.

MATERIALS AND METHODS

The data for this review were queried from Web of Science and PubMed, the keywords used were Drug targets, Target deconvolution, and Chemical Proteomics. A total of over 500 relevant articles, with a time limit from 1953 to 2022, were identified according to search strategy. Duplicate records and review articles were excluded by their titles and abstracts. Finally, we found about 120 articles matching our inclusion criteria, which covered representative research and reviews of various target discovery methods.

RESULTS

Existing target discovery methods can be grouped into either biochemical enrichment or the proteomics-screening methods, with the recent emergence of a hybrid method combining these two such as lysine reactivity profiling. The advantage of the biochemical enrichment method is the ease of operation and the comprehensive target coverage. However, most biochemical enrichment methods require a high-affinity binding of the drug to the target proteins and cannot differentiate direct/indirect targets. The proteomics-screening methods do not require drug modification but have limited protein coverage, and most of them cannot differentiate direct/indirect targets.

CONCLUSIONS

Although existing target discovery methods have greatly facilitated pharmacological research, each of these methods has advantages and disadvantages. New strategies/methods are needed to further improve both the coverage of the proteosome and the specificity.

Deep generative models for peptide design

Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of interest. Machines need to be able to understand, read, write, and eventually create new molecules. Today, this creative process relies on deep generative models, which have gained popularity since powerful deep neural networks were introduced to generative model frameworks. In recent years, they have demonstrated excellent ability to model complex distribution of real-word data (e.g., images, audio, text, molecules, and biological sequences). Deep generative models can generate data beyond those provided in training samples, thus yielding an efficient and rapid tool for exploring the massive search space of high-dimensional data such as DNA/protein sequences and facilitating the design of biomolecules with desired functions. Here, we review the emerging field of deep generative models applied to peptide science. In particular, we discuss several popular deep generative model frameworks as well as their applications to generate peptides with various kinds of properties (e.g., antimicrobial, anticancer, cell penetration, etc). We conclude our review with a discussion of current limitations and future perspectives in this emerging field.

Magnetic, Mechanical and Thermal Modeling of Superconducting, Whole-body, Actively Shielded, 3 T MRI Magnets Wound Using MgB2 Strands for Liquid Cryogen Free Operation

we present magnetic, mechanical and thermal modeling results for a 3 Tesla actively shielded whole body MRI (Magnetic Resonance Imaging) magnet consisting of coils with a square cross section of their windings. The magnet design was a segmented coil type optimized to minimize conductor length while hitting the standard field quality and DSV (Diameter of Spherical Volume) specifications as well as a standard, compact size 3 T system. It had an overall magnet length and conductor length which can lead to conduction cooled designs comparable to NbTi helium bath cooled 3 T MRI magnets. The design had a magnetic field homogeneity better than 10 ppm (part-per-million) within a DSV (Diameter of Spherical Volume) of 48 cm and the total magnet winding length of 1.37 m. A new class of MgB₂ strand especially designed for MRI applications was considered as a possible candidate for winding such magnets. This work represents the first magnetic, mechanical and thermal design for a whole-body 3 T MgB₂ short (1.37 m length) MRI magnet based on the performance parameters of existing MgB₂ wire. 3 Tesla MRI magnet can operate at 20 K at 67 % of its critical current.

Distinct soil microbial communities under Ageratina adenophora invasions

Ageratina adenophora is one of the most hazardous invasive weeds in China. It can form a single species community quickly and cause extensive ecological harm. The belowground microbial community can participate in nutrient transformation in soil and plays an important role in the invasiveness of exotic plant species. We selected sampling sites with different invasion levels of A. adenophora. The soil property and soil biogeochemical activity were measured in both bulk and rhizosphere soil under the aggressive weed A. adenophora and under adjacent native plants. The composition of bacterial communities was investigated using high-throughput 16S rRNA gene sequencing. We found that the rhizosphere habitat selectively accumulated Sphingomonas and Steroidobacter and reduced the abundance of Gaiella and Gp6 regardless of plant host. The presence of A. adenophora caused a switch in microbial composition from Aeromicrobium and Marmoricola to Reyranella and Bradyrhizobium in the bulk soil, and from Gp4, Pirellula, Lysobacter and Aridibacterrae to Reyranella and Streptomyces in the rhizosphere soil. We also revealed specific microbes that closely related with N-cycling processes. In addition, soil pH was the main factor affecting microbial communities in both bulk and rhizosphere soil. Our study confirmed that the rhizosphere environment imposed homogenous microbial communities. The invasion of A. adenophora selected specialized bacterial communities in soils and specific microbes that potentially mediated soil nutrition cycling. Our findings provide ecological explanation to explain how the underground microbes help A. adenophora invasive.

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.

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Drug-perturbed RNA sequencing data can be used to identify drug targets

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Technology-based drug-target definitions often subsume literature definitions

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Literature and screening datasets provide complementary information on drug mechanisms

A deep-learning framework for multi-level peptide-protein interaction prediction

Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Here, we present a deep learning framework for multi-level peptide-protein interaction prediction, called CAMP, including binary peptide-protein interaction prediction and corresponding peptide binding residue identification. Comprehensive evaluation demonstrated that CAMP can successfully capture the binary interactions between peptides and proteins and identify the binding residues along the peptides involved in the interactions. In addition, CAMP outperformed other state-of-the-art methods on binary peptide-protein interaction prediction. CAMP can serve as a useful tool in peptide-protein interaction prediction and identification of important binding residues in the peptides, which can thus facilitate the peptide drug discovery process.

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

[Textual research on lost ancient Chinese medical books in Bencao Tujing]

Bencao Tujing(, Illustration Classics for Materia Medica) occupies an irreplaceable academic position in the academic history of Chinese Materia Medica. It was written in the early Song Dynasty. The contents of Chinese medical books before Song Dynasty have not been revised by Song Dynasty officials, and the original appearance of earlier documents have been preserved. Domestic and foreign scholars mainly focus on the textual research of Chinese Materia medica patterns, and academic value research. The special research on the Lost ancient Chinese medical books were relatively rare. According to the names of"XX Fang"or"Someone Fang"in Bencao Tujing, about 46 kinds of Lost ancient Chinese medical books have been found. Taking Wei Zhou's Du Xing Fang, Liu Yuxi's Chuan Xin Fang, Tian Bao Dan Fang Tu (Tian Bao Dan Xing Fang) as examples, it is found that Lost ancient Chinese medical books recorded in Bencao Tujing have abundant materials and clear clues, which have further exploration space for the research and collection of Chinese medical books.

[Levels of sgRNA as a Major Factor Affecting CRISPRi Knockdown Efficiency in K562 Cells]

To determine how nuclease deactivated Cas9 (dCas9) or single-guide RNA (sgRNA) expression levels affect the knockdown efficiency of CRISPRi, we created K562 cell clones expressing KRAB-dCas9 protein either with the inducible Tet-on system or with the constitutive SFFV promotor. Single clones were selected by fluorescence-activated cell sorting (FACS) for further study. Six genes with various expression levels were targeted using lentiviral sgRNA from two libraries in four cell clones with various KRAB-dCas9 expression levels. The expression level of dCas9 protein/sgRNA levels and the knockdown efficiency were determined by flow cytometry. The cell clone with the highest KRAB-dCas9 expression level achieved effective CRISPRi knockdown. The data describing this clone were statistically different from that on other clones, indicating the strong KRAB-dCas9 expression might be a prerequisite for CRISPRi. By adopting different multiplicity of infection (MOI) in lentiviral transduction of this clone, we modified the expression level of sgRNA and found that the knockdown efficiency was neither affected by the target gene expression level nor correlated with KRAB-dCas9 levels, which remained relatively constant across all knockdown experiments (coefficient of variation = 2.2%). As an example, the following levels of the knockdowns: 74.72, 72.28 and 39.08% for mmadhc, rpia and znf148 genes, respectively, were achieved. These knockdown efficiencies correlated well with the respective sgRNA expression levels. Linear regression models built using this data indicate that the knockdown efficiency may be significantly affected by the levels of both KRAB-dCas9 and sgRNA. Notably, the sgRNA levels have greater impact, being a major factor affecting CRISPRi efficiency.

The Role of CHPD and AIMI processing on enhancing J C and transverse connectivity of in-situ MgB2 strand

Research into in-situ MgB2 strand has been focused on improvements in J C through reduction of porosity. Both of cold-high-pressure-densification (CHPD) and advanced-internal-magnesium-infiltration (AIMI) techniques can effectively remove the voids in in-situ MgB2 strands. This study shows the nature of the reduced porosity for in-situ MgB2 strands lies on increases in transverse grain connectivity as well as longitudinal connectivity. The CHPD method bi-axially applying 1.0 GPa and 1.5 GPa yielded 4.2 K J CM∥s of 9.6 × 104 A/cm2 and 8.5 × 104 A/cm2 at 5 T, respectively, with compared with 6.0 × 104 A/cm2 for typical powder-in-tube (PIT) in-situ strand. Moreover, AIMI-processed monofilamentary MgB2 strand obtained even higher J Cs and transverse grain connectivity than the CHPD strands.

EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction

Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.

DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening

Accurate identification of compound–protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug–target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI.

[Textual research on lost articles in Mei Shi Fang]

Mei Shi fang() is a lost medical prescription book. Its title came from the "book of classics and history" , a chapter of Zhenglei Bencao (, Collected Classified Materia Medica). 117 pieces of lost articles were preserved in the book. In addition to Zhenglei Bencao, a total of 50 kinds of medical books explicitly quoted some of the lost articles in Mei Shi Fang. Among them, 38 kinds of medical books did not exceed the scope of the articles of Mei Shi Fang cited in Zhenglei Bencao, 12 kinds of medical books contained the articles of Mei Shi Fang which did not quoted in Zhenglei Bencao. It is speculated that Mei Shi Fang may still exist in the Yangtze River basin from 1552 to 1578. In terms of the existing articles of Mei Shi Fang, it has academic origin with Zhouhou Beiji Fang (, Handbook of Prescriptions for Emergency).

Secure multiparty computation for privacy-preserving drug discovery

Motivation

Quantitative structure–activity relationship (QSAR) and drug–target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery.

Results

We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery.

Availability and implementation

The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks

Motivation

Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual binding affinities are needed to increase precision and reduce the number of false positive predictions.

Results

We present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson correlation coefficient between predicted and measured binding affinities by up to 23 percentage points. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide–MHC class I interactions.

Availability and implementation

ACME is available as an open source software at https://github.com/HYsxe/ACME.

Supplementary information

Supplementary data are available at Bioinformatics online.

[Shang Zhijun, a famous contemporary philogist in herbal literature research]

Mr. Shang is a famous herbalist in contemporary China and he devoted his life to the research of herbal literatures. During his lifetime, he has compiled and published a total 19 herbal literatures, 33 works of herbal literature, 268 academic papers, and more than 20 million words of handwritten herbal transcripts. In the field of herbal literature research, he has achieved fruitful results that are highly recognized by the academic communities. The research results can be roughly summarized as four aspects: the establishment of a two-line research network of herbal formulae, the research and collection of lost herbal works, the collation of the survived ancient herbal works and the textual research of famous herbal works. Many of his research results on herbal literature have been included in the modern higher education professional textbooks, and his outstanding academic achievements have opened the door for later scholars, influencing many scholars both home and abroad.

[Probe of the compiling stylistic rules and layout of Youyou Xin Shu]

Youyou Xin Shu(The New Book on Juveniles)《》is a great book of pediatrics in the Southern Song Dynasty. The book quotes a lot of literature.This paper makes an overall arrangement of the citation, sums up the features of the books cited, corrects some wrong compilations from different texts in various existing versions. Therefore, we can infer the publishing ages and time of some literature related.

Laboratory comparison of two Aphelinus mali clades for control of woolly apple aphid from Hebei Province, China

Aphelinus mali (Haldeman) is an effective natural enemy of woolly apple aphid (WAA), Eriosoma lanigerum (Hausmann). Previous studies have found that, with WAA from Shandong Province (Qingdao) as the host, there are significant differences in various biological characteristics between a Shandong clade and Liaoning clade of A. mali. The ability of the Shandong clade to control this aphid was significantly higher than that of the Liaoning clade in Shandong Province. In order to determine whether differences were caused by better adaptation of the Shandong parasitoid clade to the population of the host in that province or if it represents a more general fitness of this clade to control the host regardless of location, we compared the same parasitoid clades with hosts from Hebei Province. We found no significant differences in the developmental threshold temperature, effective accumulated temperature, fecundity, longevity, and oviposition period of the two clades, but the duration of host searching of the Shandong clade was significantly longer than that of the Liaoning clade. The instantaneous attack rate, the control ability (a/Th), the search parameter (Q) of the Shandong clade (0.0946, 0.543, 0.0725) of A. mali were higher than that of the Liaoning clade (0.0713, 0.382, 0.0381), and therefore, with WAA from Hebei Province as the host, the host adaptability of the Shandong clade of A. mali was not worse than that of the Liaoning clade, while the pest control ability of the Shandong clade was still greater than that of the Liaoning clade.

[Comparison of outcomes of two minimally invasive approaches for multi-vessel coronary revascularization]

OBJECTIVE

To compare the safety and effectiveness of two minimally invasive approaches for multi-vessel coronary revascularization.

METHODS

From August 2014 to February 2017, 70 consecutive patients who underwent minimally invasive coronary artery bypass grafting in Peking University Third Hospital were randomly divided into two groups. In one group, 40 patients underwent staged-hybrid coronary revascularization (staged-HCR) treatment; in the other group, 30 patients underwent minimally invasive total arterial revascularization with bilateral internal thoracic artery (BITA). In staged -HCR group, the patients underwent minimally invasive direct coronary artery bypass grafting (MIDCAB) and percutaneous coronary intervention (PCI) procedure for treatment of multi-vessel disease. In BITA group, the patients underwent total arterial coronary artery bypass grafting with composite "Y" BITA graft. Preoperative and postoperative data of the two groups, including postoperative blood usage, mechanical ventilation time, domiciling duration in intensive care unit (ICU), major adverse cerebral and cardiovascular event (MACCE), and postoperative coronary angiography results were compared, in order to evaluate the safety and effectiveness of these surgical approaches.

RESULTS

The preoperative characteristics of 70 patients in the two groups showed no significant difference. All the patients underwent successfully, elective minimally invasive multi-vessel coronary artery bypass grafting as scheduled preoperatively. Postoperative result showed the patients in staged-HCR group took advantages in less postoperative mechanical ventilation time [Staged-HCR group (11.2±8.7) h vs. BITA group (18.3±9.1) h, P=0.013], shorter domiciling duration in ICU [Staged-HCR group (26.29±4.05) h vs. BITA group (44.74±28.75) h, P=0.022], and less total drainage [Staged-HCR group (695.57±250.46) mL vs. BITA group (1 103.26±547.44) mL, P=0.03] than the patients in the group of minimally invasive total arterial revascularization with BITA. Postoperative in hospital coronary angiography showed satisfactory graft patency rates in both groups [97.5% in Staged-HCR group vs. 97.8% in BITA group]. No MACCE occurred in both groups during hospitalization.

CONCLUSION

Staged-HCR is a feasible method for the treatment of multi-vessel revascularization involving right coronary artery. Minimally coronary revascularization with BITA is associated with superior long-term graft patency and it's recommended for patients who could not tolerate dual-antiplatelet therapy. This study shows that both minimally invasive surgical approaches are safe and effective for treatment of patients with multi-vessel coronary artery disease.

Architecture and Transport Properties of Multifilamentary MgB2 Strands for MRI and Low AC Loss Applications

Standard in-situ type MgB2 strands manufactured by Hyper Tech Inc have 19 - 36 subelements, a monel outer sheath, and a Cu interfilamentary matrix. Typical transport Jc s of the strands are 2×105 A/cm2 with n-values of 20 - 30 at 4.2 K and 5 T. This work introduces two new MgB2 conductor designs. First, a new class of MgB2 strand is designed for magnetic resonance imaging applications. This type has a higher Cu content designed to enhance protection of a magnet wound with it, and a larger diameter to increase the critical current. Second, a new class of low AC loss MgB2 strand with high filament count and a high resistance matrix is discussed. Transport properties at 4.2 K and fields up to 10 T are reported. Optical techniques are used to study the macro- and micro-structures of these MgB2 strands.

Identification of the crucial genes in the elimination and survival process of Salmonella enterica ser. Pullorum in the chicken spleen

Salmonella enterica ser. Pullorum is one of the most easily re-infecting pathogens in poultry production because of its mechanism of escaping from immune elimination. We used the transcriptome method to investigate the variation in gene expression in chicken spleen resulting from the interaction between hosts and S. Pullorum in the survival process. The expression of various genes related to the maturation and activation of B cells was activated before S. Pullorum was eliminated, which might help S. Pullorum escape from the elimination process. The suppression of some genes involved in the fusion of autophagosomes and lysosomes, such as MYO6, was identified and may be regulated by the secretion systems of S. Pullorum. In addition, a large proportion of these differentially expressed genes could be localized in the identified quantitative trait loci regions associated with the antibody response to bacteria. Collectively, these identified genes provided an outline for further understanding the interaction between chicken immune cells and S. Pullorum in chicken spleen.

DTC chemotherapy regimen is associated with higher incidence of premature ovarian failure in women of reproductive age with breast cancer

OBJECTIVE

Different chemotherapy regimens may contribute differently to the development of Premature Ovarian Failure (POF) in women of reproductive age with breast cancer. Here we evaluated how two different chemotherapy regimens, CAF (tegafur + pirarubicin + ifosfamide) and DTC (docetaxel + pirarubicin + ifosfamide), affect the development of POF.

PATIENTS AND METHODS

We enrolled 164 women of reproductive age with breast cancer (mean ± SD age of 34.56 ± 9.48 years). The patients were divided into two groups, which were respectively treated with CAF (n = 89) or DTC (n = 75) chemotherapy regimen. Both study groups were comparable in all analyzed characteristics at baseline. Patients were treated with respective chemotherapy regimen for 6 months and followed up for over 12 months after completion of chemotherapy. Study outcomes were occurrence rates of POF, menstrual status and recovery after completion of chemotherapy, and serum levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), and oestradiol (E2).

RESULTS

At 6 months after completion of chemotherapy, POF incidence rates were significantly lower in the CAF group. Furthermore, the proportion of patients with eumenorrhea, menstrual disorders or chemotherapy-induced amenorrhea in this study group was also significantly different from the DTC group. Similarly, adverse changes of serum levels of FSH, LH and E2 were less pronounced in the CAF group.

CONCLUSIONS

Both tested chemotherapy regimens can cause POF; however, adverse effects of DCT chemotherapy regimen on ovarian function are more pronounced than those by CAF chemotherapy regimen.

Single nucleotide polymorphism screening, molecular characterization, and evolutionary aspects of chicken Piwi genes

The P-element-induced wimpy testis (Piwi) gene is involved in germline stem cell self-renewal, meiosis, RNA silencing, and transcriptional regulation. Piwi genes are relatively well conserved in many species, but their function in poultry species is unclear. In this study, Piwi genes were sequenced using a target-sequence capture assay in quail and 28 breeds of chicken. Single nucleotide polymorphisms (SNPs) and evolutionary aspects of these chicken breeds were then analyzed. We found that SNP sites existed mainly in the introns of a few chicken breeds, and we selected an SNP on intron 4 for further verification by Sanger sequencing, the results of which were similar to those obtained by the target-capture sequencing assay. The evolutionary analysis revealed that there were more mutations in the Chahua and Leghorn breeds than in the other breeds, and that the phylogenetic tree was divided into four main categories that suggested that Piwi is evolutionarily conserved, and mutations in the introns might be associated with gametogenesis. The screened SNPs can be used as candidate markers for Piwi, and our results provide basic information for the further study of Piwi function in poultry.

Fertility clinicians and infertile patients in China have different preferences in fertility care

STUDY QUESTION

Do the preferences for fertility care of infertile Chinese patients differ from those of fertility care providers?

SUMMARY ANSWER

Infertile Chinese patients attached the greatest importance to physicians' attitude but, in contrast, fertility care providers in China considered treatment effectiveness to be the most important factor in fertility care.

WHAT IS KNOWN ALREADY

In Europe, physicians underestimate the importance of patient-centred infertility care.

STUDY DESIGN, SIZE, DURATION

A conjoint survey was distributed among 417 female infertile Chinese patients and 83 fertility care providers from February 2013 to August 2013.

PARTICIPANTS/MATERIALS, SETTING, METHODS

In this pilot study, 389 patients and 83 fertility care providers completed the survey at three reproductive medicine centres. Rating-based conjoint analysis was performed to elicit patients' and their caregivers' preferences regarding fertility care. Cluster analysis was used to explore the heterogeneity among patients' preferences.

MAIN RESULTS AND THE ROLE OF CHANCE

When searching for fertility care, patients valued the physicians' attitude the most, followed by success rates, distance from home to the fertility centre, physician continuity throughout the treatment period and type of fertility centre. Fertility care providers considered success rates (effectiveness) to be the most important factor when recommending a fertility centre. Fertility patients and care providers had significantly different views on the value of treatment effectiveness, physician attitude and physician continuity (P-values <0.05). Cluster analysis revealed that patients' preferences were heterogeneous.

LIMITATIONS, REASONS FOR CAUTION

The sample size is relatively small, and there is insufficient power for heterogeneity analysis. Two levels for each of five design factors (2(5)) may not fully capture the characteristics of realistic fertility centres. Rating-based conjoint analysis could be inferior to choice-based conjoint analysis in terms of predictive accuracy.

WIDER IMPLICATIONS OF THE FINDINGS

Fertility care providers in China significantly underestimate the importance of patient-centredness to infertile patients. To deliver optimal fertility care to infertile Chinese patients, fertility care providers need to understand the importance of patient-centred care, such as a friendly attitude, sympathy for patients' suffering, respect for patients' right to informed consent and a transparent treatment process.

STUDY FUNDING/COMPETING INTEREST(S)

This study was not funded, and there are no conflicts of interest.

TRIAL REGISTRATION NUMBER

None.

Comparison of three lymph node staging schemes for predicting outcome in patients with gastric cancer

BACKGROUND

Several node staging schemes have been proposed for gastric cancer. The optimal system remains controversial.

METHODS

Patients with gastric cancer were identified from the Surveillance, Epidemiology, and End Results (SEER) database, and a Chinese patient cohort was used for independent validation. The prognostic performance of three node staging schemes was compared, involving a number-based scheme (pN), ratio-based scheme (rN) and log odds of positive lymph nodes scheme (LODDS).

RESULTS

There were 12 443 patients in the SEER database and 866 in the Chinese cohort. LODDS provided better discriminatory capacity and higher predictive accuracy than either pN or rN, for patients with gastric cancer in both the SEER database and the Chinese cohort. The multivariable model using the LODDS classification was significantly more predictive than the pN classification. LODDS suffered much less from stage migration and was able efficiently to discriminate the heterogeneity for patients with no nodes involved or all nodes involved, whereas the pN and rN schemes could not.

CONCLUSION

LODDS showed a clear prognostic superiority over both pN and rN schemes. It could serve as an important reference for the tumour node metastasis (TNM) node classification.