CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis.

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Multiphysical graph neural network (MP-GNN) for COVID-19 drug design.

Graph neural networks (GNNs) are the most promising deep learning models that can revolutionize non-Euclidean data analysis. However, their full potential is severely curtailed by poorly represented molecular graphs and features. Here, we propose a multiphysical graph neural network (MP-GNN) model based on the developed multiphysical molecular graph representation and featurization. All kinds of molecular interactions, between different atom types and at different scales, are systematically represented by a series of scale-specific and element-specific graphs with distance-related node features. From these graphs, graph convolution network (GCN) models are constructed with specially designed weight-sharing architectures. Base learners are constructed from GCN models from different elements at different scales, and further consolidated together using both one-scale and multi-scale ensemble learning schemes. Our MP-GNN has two distinct properties. First, our MP-GNN incorporates multiscale interactions using more than one molecular graph. Atomic interactions from various different scales are not modeled by one specific graph (as in traditional GNNs), instead they are represented by a series of graphs at different scales. Second, it is free from the complicated feature generation process as in conventional GNN methods. In our MP-GNN, various atom interactions are embedded into element-specific graph representations with only distance-related node features. A unique GNN architecture is designed to incorporate all the information into a consolidated model. Our MP-GNN has been extensively validated on the widely used benchmark test datasets from PDBbind, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016. Our model can outperform all existing models as far as we know. Further, our MP-GNN is used in coronavirus disease 2019 drug design. Based on a dataset with 185 complexes of inhibitors for severe acute respiratory syndrome coronavirus (SARS-CoV/SARS-CoV-2), we evaluate their binding affinities using our MP-GNN. It has been found that our MP-GNN is of high accuracy. This demonstrates the great potential of our MP-GNN for the screening of potential drugs for SARS-CoV-2. Availability: The Multiphysical graph neural network (MP-GNN) model can be found in https://github.com/Alibaba-DAMO-DrugAI/MGNN. Additional data or code will be available upon reasonable request.

Treatment outcomes and prognostic factors of the apical barrier technique with premixed calcium silicate-based putty in necrotic permanent teeth with open apices: a retrospective cohort study with up to six years follow-up.

OBJECTIVES: To evaluate treatment outcomes of the apical barrier technique with premixed calcium silicate-based putty for treating necrotic permanent teeth with open apices and to identify prognostic factors. MATERIALS AND METHODS: Permanent teeth with necrotic pulps and open apices treated by the apical barrier technique with premixed calcium silicate-based putty, with a minimum follow-up of 12 months, were included. Treatment outcomes were based on clinical signs, symptoms, and radiographic evaluation. The treatment outcome was dichotomized into success or failure according to strict and loose criteria. The chi-square test (or Fisher's exact test) and multiple logistic regression analysis were used to evaluate possible prognostic factors associated with treatment outcomes. RESULTS: Seventy-four teeth with a follow-up time of 12-72 months (mean, 25.74 ± 14.36 months) were included in the final evaluation. The success rate was 97.30% using the loose criteria and 66.22% using the strict criteria. Multiple logistic regression analysis indicated that the size of pre-operative periapical lesion (≥ 5 mm) (odds ratio [OR]: 18.96; P = 0.0153) and root canal underfilling (OR: 8.341; P = 0.0448) were significant predictors for treatment failure under the strict criteria. CONCLUSION: The apical barrier technique with premixed calcium silicate-based putty is a highly successful procedure for treating necrotic permanent teeth with open apices after an observation period of up to 6 years. Treatment success under the strict criteria is primarily affected by the size of the pre-operative periapical lesion and the apical extent of root-filling. CLINICAL RELEVANCE: Careful case selection and ensuring adequate root filling quality are essential to the successful outcome of the apical barrier technique with premixed calcium silicate-based putty.

Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer: A Retrospective Multicenter Study.

OBJECTIVE: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. BACKGROUND: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. METHODS: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker-DeepCT-PDAC-by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. RESULTS: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50-2.75; HR: 2.47, CI: 1.35-4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89-3.28; HR: 2.15, CI: 1.14-4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19-0.64), but did not affect OS in the subgroup with high risk. CONCLUSIONS: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

Targeted co-delivery of PD-L1 monoclonal antibody and sorafenib to circulating tumor cells via platelet-functionalized nanocarriers.

BACKGROUND: Circulating tumor cells (CTCs) can adsorb and activate platelets to form a microthrombus protective barrier around them, so that therapeutic drugs and immune cells cannot effectively kill CTCs. The platelet membrane (PM) bionic carrying drug system has the powerful ability of immune escape, and can circulate in the blood for a long time. MATERIALS AND METHODS: we developed platelet membrane coated nanoparticles (PM HMSNs) to improve the precise delivery of drugs to tumor sites and to achieve more effective immunotherapy combined with chemotherapy strategy. RESULTS: Successfully prepared aPD-L1-PM-SO@HMSNs particles, whose diameter is 95-130 nm and presenting the same surface protein as PM. Laser confocal microscopy and flow cytometry experimental results showed that the fluorescence intensity of aPD-L1-PM-SO@HMSNs was greater than SO@HMSNs that are not coated by PM. Biodistribution studies in H22 tumor-bearing mice showed that due to the combined action of the active targeting effect and the EPR effect, the high accumulation of aPD-L1-PM-SO@HMSNs in the local tumor was more effective in inhibiting tumor growth than other groups of therapeutic agents. CONCLUSION: Platelet membrane biomimetic nanoparticles have a good targeted therapeutic effect, which can effectively avoid immune clearance and have little side effects. It provides a new direction and theoretical basis for further research on targeted therapy of CTCs in liver cancer.

Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma.

Background Although deep learning has brought revolutionary changes in health care, reliance on manually selected cross-sectional images and segmentation remain methodological barriers. Purpose To develop and validate an automated preoperative artificial intelligence (AI) algorithm for tumor and lymph node (LN) segmentation with CT imaging for prediction of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, patients with surgically resected, pathologically confirmed PDAC underwent multidetector CT from January 2015 to April 2020. Three models were developed, including an AI model, a clinical model, and a radiomics model. CT-determined LN metastasis was diagnosed by radiologists. Multivariable logistic regression analysis was conducted to develop the clinical and radiomics models. The performance of the models was determined on the basis of their discrimination and clinical utility. Kaplan-Meier curves, the log-rank test, or Cox regression were used for survival analysis. Results Overall, 734 patients (mean age, 62 years ± 9 [SD]; 453 men) were evaluated. All patients were split into training (n = 545) and validation (n = 189) sets. Patients who had LN metastasis (LN-positive group) accounted for 340 of 734 (46%) patients. In the training set, the AI model showed the highest performance (area under the receiver operating characteristic curve [AUC], 0.91) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.58, 0.76, and 0.71, respectively. In the validation set, the AI model showed the highest performance (AUC, 0.92) in the prediction of LN metastasis, whereas the radiologists and the clinical and radiomics models had AUCs of 0.65, 0.77, and 0.68, respectively (P < .001). AI model-predicted positive LN metastasis was associated with worse survival (hazard ratio, 1.46; 95% CI: 1.13, 1.89; P = .004). Conclusion An artificial intelligence model outperformed radiologists and clinical and radiomics models for prediction of lymph node metastasis at CT in patients with pancreatic ductal adenocarcinoma. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chu and Fishman in this issue.

Synthesis of Cyclopenta[c]quinolines by Palladium-Catalyzed Cyclization of 3-Bromoindoles with Internal Alkynes via Spirocyclic Cyclopentadiene Intermediates.

A novel method for the construction of a cyclopenta[c]quinoline ring via cyclization of 3-bromoindoles with internal alkynes in the presence of palladium is described. The formation of the cyclopenta[c]quinoline ring is proposed from a double [1,5] carbon sigmatropic rearrangement of the spirocyclic cyclopentadiene intermediate, which is generated in situ from the cyclization of 3-bromoindoles with internal alkynes involving a sequential double alkyne insertion into the carbon-palladium bond and dearomatization of indole. The present studies have developed a novel ring-expansion reaction of the pyrrole ring to pyridine via one carbon insertion into the C2-C3 bond of indoles and provided a simple and distinct route for the construction of tricyclic fused-quinoline derivatives that are not easy to access with conventional methods.

Evaluating the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis after nonsurgical treatment failure.

AIM: To determine the efficacy of endodontic microsurgery for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after failed nonsurgical treatment. METHODOLOGY: Eighty teeth in 78 patients were subjected to endodontic microsurgery. All patients were clinically and radiologically examined 1 year postoperatively. The data were statistically analyzed using SPSS 27.0 software. RESULTS: Of the 80 teeth in 78 patients, periapical lesions had disappeared in 77 teeth at 1-year postoperative follow-up, with a success rate of approximately 96.3% (77/80). The efficacy of endodontic microsurgery was not affected by sex, age, extent of periapical lesions, and presence of the sinus tract. Between-group differences were not statistically significant (P > 0.05). CONCLUSIONS: Endodontic microsurgery can be an effective alternative treatment option for teeth with an undeveloped root apex and periapical periodontitis caused by an abnormal central cusp fracture after nonsurgical treatment failure.

Bimetallic Cage-Based Metal-Organic Frameworks for Electrochemical Hydrogen Evolution Reaction with Enhanced Activity.

A cage-based metal-organic framework (Ni-NKU-101) with biphenyl-3,3',5,5'-tetracarboxylic acid was synthesized via solvothermal method. Ni-NKU-101 contains two types of cages based on trinuclear and octa-nuclear nickel-clusters that are connected with each other by the 4-connected ligands, to form a 3D framework with a new topology. A mixed-metal strategy was used to synthesize isostructural bimetallic MOFs of Mx Ni1-x -NKU-101 (M=Mn, Co, Cu, Zn). The electrocatalytic studies showed that the hydrogen evolution reaction (HER) activity of Cux Ni1-x -NKU-101 is much higher than that of other Mx Ni1-x -NKU-101 catalysts in acidic aqueous solution, owing to the synergistic effect of the bimetallic centers. The optimized Cu0.19 Ni0.81 -NKU-101 has an overpotential of 324 mV at 10 mA cm-2 and a Tafel slope of 131 mV dec-1 . The mechanism of HER activity over these bimetallic MOF-based electrocatalysts are discussed in detail.

Copper(I)-Catalyzed Syntheses of Benzo[b]fluorenes by the Cascade Reactions of 2-Alkynylbenzaldehyde N-Tosylhydrazones and Aromatic Terminal Alkynes.

Benzo[b]fluorenes were synthesized via CuI-catalyzed cascade reactions of 2-alkynylbenzaldehyde N-tosylhydrazones and aromatic terminal alkynes in the presence of base including the formation of the benzoenyne-allene intermediate in situ and its Schmittel cyclization. Density functional theory calculation was performed to give the energy difference for the formation of 5-phenyl-11H-benzo[b]fluorene and 1,2-diphenyl-1H-cyclobuta[a]indene from the proposed diradical intermediates.

Exosomal lncRNA TUG1 from cancer-associated fibroblasts promotes liver cancer cell migration, invasion, and glycolysis by regulating the miR-524-5p/SIX1 axis.

BACKGROUND: Increasing evidence suggests that taurine upregulated gene 1 (TUG1) is crucial for tumor progression; however, its role in hepatocellular carcinoma (HCC) and the underlying mechanisms are not well characterized. METHODS: The expression levels of TUG1, miR-524-5p, and sine oculis homeobox homolog 1 (SIX1) were determined using quantitative real-time PCR. The regulatory relationships were confirmed by dual-luciferase reporter assay. Cell proliferation and invasion were assessed using Cell Counting Kit 8 and transwell assays. Glucose uptake, cellular levels of lactate, lactate dehydrogenase (LDH), and adenosine triphosphate (ATP) were detected using commercially available kits. Silencing of TUG1 or SIX1 was performed by lentivirus transduction. Protein levels were measured by immunoblotting. RESULTS: Cancer-associated fibroblasts (CAFs)-secreted exosomes promoted migration, invasion, and glycolysis in HepG2 cells by releasing TUG1. The promotive effects of CAFs-secreted exosomes were attenuated by silencing of TUG1. TUG1 and SIX1 are targets of miR-524-5p. SIX1 knockdown inhibited the promotive effects of miR-524-5p inhibitor. Silencing of TUG1 suppressed tumor growth and lung metastasis and therefore increased survival of xenograft model mice. We also found that TUG1 and SIX1 were increased in HCC patients with metastasis while miR-524-5p was decreased in HCC patients with metastasis. CONCLUSIONS: CAFs-derived exosomal TUG1 promoted migration, invasion, and glycolysis in HCC cells via the miR-524-5p/SIX1 axis. These findings may help establish the foundation for the development of therapeutics strategies and clinical management for HCC in future.

Dual XH-π Interaction of Hexafluoroisopropanol with Arenes.

The dual XH (OH and CH) hydrogen-bond-donating property of 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) and the strong dual XH-π interaction with arenes were firstly disclosed by theoretical studies. Here, the high accuracy post-Hartree-Fock methods, CCSD(T)/CBS, reveal the interaction energy of HFIP/benzene complex (-7.22 kcal/mol) and the contribution of the electronic correlation energy in the total interaction energy. Strong orbital interaction between HFIP and benzene was found by using the DFT method in this work to disclose the dual XH-π intermolecular orbital interaction of HFIP with benzene-forming bonding and antibonding orbitals resulting from the orbital symmetry of HFIP. The density of states and charge decomposition analyses were used to investigate the orbital interactions. Isopropanol (IP), an analogue of HFIP, and chloroform (CHCl3) were studied to compare them with the classical OH-π, and non-classical CH-π interactions. In addition, the influence of the aggregating effect of HFIP, and the numbers of substituted methyl groups in benzene rings were also studied. The interaction energies of HFIP with the selected 24 common organic compounds were calculated to understand the role of HFIP as solvent or additive in organic transformation in a more detailed manner. A single-crystal X-ray diffraction study of hexafluoroisopropyl benzoate further disclosed and confirmed that the CH of HFIP shows the non-classical hydrogen-bond-donating behavior.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images.

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine, etc. Results In the external validation, the lesion-level PPV and sensitivity of the model were 57.9% and 67.0%, respectively. On average, the model detected 2.1 findings per set, and among them, 0.9 were false positives. ULDor worked well for detecting liver lesions, with a PPV of 78.9% and a sensitivity of 92.7%, followed by kidney, with a PPV of 70.0% and a sensitivity of 58.3%. In internal validation with NLST test set, ULDor obtained a PPV of 75.3% and a sensitivity of 52.0% despite the relatively high noise level of soft tissue on images. Conclusions The performance tests of ULDor with the external real-world data have shown its high effectiveness in multiple-purposed detection for lesions in certain organs. With further optimisation and iterative upgrades, ULDor may be well suited for extensive application to external data.

Heat shock transcription factor 1 regulates exercise-induced myocardial angiogenesis after pressure overload via HIF-1α/VEGF pathway.

Exercise training is believed to have a positive effect on cardiac hypertrophy after hypertension. However, its mechanism is still not fully understood. Herein, our findings suggest that heat shock transcription factor 1 (HSF1) improves exercise-initiated myocardial angiogenesis after pressure overload. A sustained narrowing of the diagonal aorta (TAC) and moderately- intense exercise training protocol were imposed on HSF1 heterozygote (KO) and their littermate wild-type (WT) male mice. After two months, the cardiac function was assessed using the adaptive responses to exercise training, or TAC, or both of them such as catheterization and echocardiography. The HE stains assessed the area of myocyte cross-sectional. The Western blot and real-time PCR measured the levels of expression for heat shock factor 1 (HSF1), vascular endothelial growth factor (VEGF) and hypoxia inducible factor-1 alpha (HIF-1α) in cardiac tissues. The anti-CD31 antibody immunohistochemical staining was done to examine how exercise training influenced cardiac ontogeny. The outcome illustrated that exercise training significantly improved the cardiac ontogeny in TAC mice, which was convoyed by elevated levels of expression for VEGF and HIF-1α and preserved the heart microvascular density. More importantly, HSF1 deficiency impaired these effects induced by exercise training in TAC mice. In conclusion, exercise training encourages cardiac ontogeny by means of HSF1 activation and successive HIF-1α/VEGF up-regulation in endothelial cells during continued pressure overload.

Base-Promoted Annulation of Amidoximes with Alkynes: Simple Access to 2,4-Disubstituted Imidazoles.

An efficient construction of imidazole ring by a Cs2CO3-promoted annulation of amidoximes with terminal alkynes in DMSO has been developed. This protocol provides a simple synthetic route with high atom-utilization for the synthesis of 2,4-disubstituted imidazoles in good yields under transition-metal-free and ligand-free conditions. Internal alkynes can also undergo the annulation to give 2,4,5-trisubstituted imidazoles.

Treatment with metformin prevents pre-eclampsia by suppressing migration of trophoblast cells via modulating the signaling pathway of UCA1/miR-204/MMP-9.

Metformin has been reported to be involved in the pathogenesis of PE (pre-eclampsia). This study aims to investigate the molecular mechanism of how UCA1 interferes with MMP9 expression under the influence of metformin, which contributes to the development of pre-eclampsia. Transwell assay was utilized to examine effect of Met (Metformin) on cell migration, and real-time PCR, western-blot analysis and IHC (Immunohistochemistry) were carried out to determine effect of Met on expressions of UCA1, miR-204 and MMP9, as well as compare UCA1, miR-204 and MMP9 level in different groups. Computational analysis and luciferase assay were performed to detect interaction among UCA1, miR-204 and MMP9. Met evidently inhibited the migration of HUVEC and HPASMC cells, and UCA1 and MMP9 levels showed a stepwise decline, while miR-204 level displayed a gradual increase as the concentration of Met increased when compared with the negative controls. Result of computational analysis showed that miR-204 directly bound to UCA1. Meanwhile, MMP9 was identified as a virtual target gene of miR-204. Result of protein were observed in pre-eclampsia (+) group compared with pre-eclampsia (-) group, while miR-204 level in pre-eclampsia (+) group was significantly lower than pre-eclampsia (-) group. Taken together, we found that administration of metformin prevents pre-eclampsia by suppressing migration of trophoblast cells via modulating the signaling pathway of UCA1/miR-204/MMP-9.

IL1A polymorphisms is a risk factor for colorectal cancer in Chinese Han population: a case control study.

BACKGROUND: Colorectal cancer (CRC) is one of the most common cancers worldwide, and genetic variations exert distinct roles in its pathogenesis. Single nucleotide polymorphisms (SNPs) in interleukin 1 alpha (IL1A) were reported to be correlated to the susceptibility of diverse cancers. The aim of this study was to assess the association of IL1A SNPs with the risk of colorectal cancer in a Chinese Han population. METHODS: To evaluate the correlation between IL1A polymorphisms and CRC risk, Agena MassARRAY platform was used for genotype determination among 248 CRC patients and 463 controls. The relationships between IL1A variants and CRC susceptibility were examined by logistic regression analysis. Stratified analysis was conducted for the association detection in males and females. Haplotype construction and analysis were applied to evaluate the potential relationship between the genetic block and the risk of CRC. SNP functional exploration was performed with available bioinformatics datasets. RESULTS: After adjusting for age and gender, the "AA" genotype of rs2856838 exhibited a risk association with colorectal cancer in the recessive model (adjusted OR = 1.98, 95% CI: 1.05-3.72, p = 0.036). With stratified analysis, the recessive models of rs3783550 (OR = 2.17, 95% CI: 1.03-4.60, p = 0.043), rs2856838 (OR = 2.58, 95% CI: 1.13-5.87, p = 0.024), rs1609682 (OR = 2.20, 95% CI: 1.04-4.65, p = 0.040), and rs3783521 (OR = 2.13, 95% CI: 1.01-4.49, p = 0.048) revealed significant relationships between these variants and an increased CRC risk only in females. Bioinformatics analysis also revealed the putative functions of the selected SNPs. CONCLUSIONS: This study demonstrated that rs2856838 could influence the susceptibility to CRC in Chinese Han population from northwest China. IL1A variants rs3783550, rs2856838, rs1609682, and rs3783521 were associated with CRC risk only in females.

PPIM: A Protein-Protein Interaction Database for Maize.

Maize (Zea mays) is one of the most important crops worldwide. To understand the biological processes underlying various traits of the crop (e.g. yield and response to stress), a detailed protein-protein interaction (PPI) network is highly demanded. Unfortunately, there are very few such PPIs available in the literature. Therefore, in this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface. We believe that the PPIM resource can help biologists better understand the maize crop.

Patterns of chlamydia testing in different settings and implications for wider STI diagnosis and care: a probability sample survey of the British population.

BACKGROUND: Following widespread rollout of chlamydia testing to non-specialist and community settings in the UK, many individuals receive a chlamydia test without being offered comprehensive STI and HIV testing. We assess sexual behaviour among testers in different settings with a view to understanding their need for other STI diagnostic services. METHODS: A probability sample survey of the British population undertaken 2010-2012 (the third National Survey of Sexual Attitudes and Lifestyles). We analysed weighted data on chlamydia testing (past year), including location of most recent test, and diagnoses (past 5 years) from individuals aged 16-44 years reporting at least one sexual partner in the past year (4992 women, 3406 men). RESULTS: Of the 26.8% (95% CI 25.4% to 28.2%) of women and 16.7% (15.5% to 18.1%) of men reporting a chlamydia test in the past year, 28.4% of women and 41.2% of men had tested in genitourinary medicine (GUM), 41.1% and 20.7% of women and men respectively tested in general practice (GP) and the remainder tested in other non-GUM settings. Women tested outside GUM were more likely to be older, in a relationship and to live in rural areas. Individuals tested outside GUM reported fewer risk behaviours; nevertheless, 11.0% (8.6% to 14.1%) of women and 6.8% (3.9% to 11.6%) of men tested in GP and 13.2% (10.2% to 16.8%) and 9.6% (6.5% to 13.8%) of women and men tested in other non-GUM settings reported 'unsafe sex', defined as two or more partners and no condom use with any partner in the past year. Individuals treated for chlamydia outside GUM in the past 5 years were less likely to report an HIV test in that time frame (women: 54.5% (42.7% to 65.7%) vs 74.1% (65.9% to 80.9%) in GUM; men: 23.9% (12.7% to 40.5%) vs 65.8% (56.2% to 74.3%)). CONCLUSIONS: Most chlamydia testing occurred in non-GUM settings, among populations reporting fewer risk behaviours. However, there is a need to provide pathways to comprehensive STI care to the sizeable minority at higher risk.