Palladium-Catalyzed Regioselective B(3,5)-Dialkenylation and B(4)-Alkenylation of o-Carboranes.

Picolyl group directed B(3,5)-dialkenylation and B(4)-monoalkenylation of o-carboranes has been developed with a very low palladium catalyst loading. The degree of substitution is determined by the cage C(2)-substituents due to steric reasons. On the basis of experimental results, a plausible mechanism is proposed including electrophilic palladation and alkyne insertion followed by protonation.

CCR2 is a potential therapeutic target in peri-implantitis.

AIM: CCR2 (C-C chemokine receptor type 2) plays a crucial role in inflammatory and bone metabolic diseases; however, its role in peri-implantitis remains unclear. This study aimed to explore whether CCR2 contributes to peri-implantitis and the treatment effects of cenicriviroc (CVC) on peri-implant inflammation and bone resorption. MATERIALS AND METHODS: The expression of CCR2 was studied using clinical tissue analysis and an in vivo peri-implantitis model. The role of CCR2 in promoting inflammation and bone resorption in peri-implantitis was evaluated in Ccr2-/- mice and wild-type mice. The effect of CVC on peri-implantitis was evaluated using systemic and local dosage forms. RESULTS: Human peri-implantitis tissues showed increased CCR2 and CCL2 levels, which were positively correlated with bone loss around the implants. Knocking out Ccr2 in an experimental model of peri-implantitis resulted in decreased monocyte and macrophage infiltration, reduced pro-inflammatory cytokine generation and impaired osteoclast activity, leading to reduced inflammation and bone loss around the implants. Treatment with CVC ameliorated bone loss in experimental peri-implantitis. CONCLUSIONS: CCR2 may be a potential target for peri-implantitis treatment by harnessing the immune-inflammatory response to modulate the local inflammation and osteoclast activity.

Dynamic peripheral blood immune cell markers for predicting the response of patients with metastatic cancer to immune checkpoint inhibitors.

PURPOSE: Immune checkpoint inhibitors (ICIs) have shown durable responses in various malignancies. However, the response to ICI therapy is unpredictable, and investigation of predictive biomarkers needs to be improved. EXPERIMENTAL DESIGN: In total, 120 patients receiving ICI therapy and 40 patients receiving non-ICI therapy were enrolled. Peripheral blood immune cell markers (PBIMs), as liquid biopsy biomarkers, were analyzed by flow cytometry before ICI therapy, and before the first evaluation. In the ICI cohort, patients were randomly divided into training (n = 91) and validation (n = 29) cohorts. Machine learning algorithms were applied to construct the prognostic and predictive immune-related models. RESULTS: Using the training cohort, a peripheral blood immune cell-based signature (BICS) based on four hub PBIMs was developed. In both the training and the validation cohorts, and the whole cohort, the BICS achieved a high accuracy for predicting overall survival (OS) benefit. The high-BICS group had significantly shorter progression-free survival and OS than the low-BICS group. The BICS demonstrated the predictive ability of patients to achieve durable clinical outcomes. By integrating these PBIMs, we further constructed and validated the support vector machine-recursive and feature elimination classifier model, which robustly predicts patients who will achieve optimal clinical benefit. CONCLUSIONS: Dynamic PBIM-based monitoring as a noninvasive, cost-effective, highly specific and sensitive biomarker has broad potential for prognostic and predictive utility in patients receiving ICI therapy.

Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma.

BACKGROUND: The metastatic vascular patterns of hepatocellular carcinoma (HCC) are mainly microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC). However, most existing VETC-related radiological studies still focus on the prediction of VETC status. PURPOSE: This study aimed to build and compare VETC-MVI related models (clinical, radiomics, and deep learning) associated with recurrence-free survival of HCC patients. STUDY TYPE: Retrospective. POPULATION: 398 HCC patients (349 male, 49 female; median age 51.7 years, and age range: 22-80 years) who underwent resection from five hospitals in China. The patients were randomly divided into training cohort (n = 358) and test cohort (n = 40). FIELD STRENGTH/SEQUENCE: 3-T, pre-contrast T1-weighted imaging spoiled gradient recalled echo (T1WI SPGR), T2-weighted imaging fast spin echo (T2WI FSE), and contrast enhanced arterial phase (AP), delay phase (DP). ASSESSMENT: Two radiologists performed the segmentation of HCC on T1WI, T2WI, AP, and DP images, from which radiomic features were extracted. The RFS related clinical characteristics (VETC, MVI, Barcelona stage, tumor maximum diameter, and alpha fetoprotein) and radiomic features were used to build the clinical model, clinical-radiomic (CR) nomogram, deep learning model. The follow-up process was done 1 month after resection, and every 3 months subsequently. The RFS was defined as the date of resection to the date of recurrence confirmed by radiology or the last follow-up. Patients were followed up until December 31, 2022. STATISTICAL TESTS: Univariate COX regression, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier curves, log-rank test, C-index, and area under the curve (AUC). P < 0.05 was considered statistically significant. RESULTS: The C-index of deep learning model achieved 0.830 in test cohort compared with CR nomogram (0.731), radiomic signature (0.707), and clinical model (0.702). The average RFS of the overall patients was 26.77 months (range 1-80 months). DATA CONCLUSION: MR deep learning model based on VETC and MVI provides a potential tool for survival assessment. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

Combination of Sequencing Batch Operation and A/O Process to Achieve Partial Mainstream Anammox: Pilot-Scale Demonstration and Microbial Ecological Mechanism.

In this study, sequencing batch operation was successfully combined with a pilot-scale anaerobic biofilm-modified anaerobic/aerobic membrane bioreactor to achieve anaerobic ammonium oxidation (anammox) without inoculation of anammox aggregates for municipal wastewater treatment. Both total nitrogen and phosphorus removal efficiencies of the reactor reached up to 80% in the 250-day operation, with effluent concentrations of 4.95 mg-N/L and 0.48 mg-P/L. In situ enrichment of anammox bacteria with a maximum relative abundance of 7.86% was observed in the anaerobic biofilm, contributing to 18.81% of nitrogen removal, with denitrification being the primary removal pathway (38.41%). Denitrifying phosphorus removal (DPR) (40.54%) and aerobic phosphorus uptake (48.40%) played comparable roles in phosphorus removal. Metagenomic sequencing results showed that the biofilm contained significantly lower abundances of NO-reducing functional genes than the bulk sludge (p < 0.01), favoring anammox catabolism in the former. Interactions between the anammox bacteria and flanking community were dominated by cooperation behaviors (e.g., nitrite supply, amino acids/vitamins exchange) in the anaerobic biofilm community network. Moreover, the hydrolytic/fermentative bacteria and endogenous heterotrophic bacteria (Dechloromonas, Candidatus competibacter) were substantially enriched under sequencing batch operation, which could alleviate the inhibition of anammox bacteria by complex organics. Overall, this study provides a feasible and promising strategy for substantially enriching anammox bacteria and achieving partial mainstream anammox as well as DPR.

CCL2 is a key regulator and therapeutic target for periodontitis.

AIM: Our previous study revealed that the C-C motif chemokine receptor 2 (CCR2) is a promising target for periodontitis prevention and treatment. However, CCR2 is a receptor with multiple C-C motif chemokine ligands (CCLs), including CCL2, CCL7, CCL8, CCL13 and CCL16, and which of these ligands plays a key role in periodontitis remains unclear. The aim of the present study was to explore the key functional ligand of CCR2 in periodontitis and to evaluate the potential of the functional ligand as a therapeutic target for periodontitis. MATERIALS AND METHODS: The expression levels and clinical relevance of CCR2, CCL2, CCL7, CCL8, CCL13 and CCL16 were studied using human samples. The role of CCL2 in periodontitis was evaluated by using CCL2 knockout mice and overexpressing CCL2 in the periodontium. The effect of local administration of bindarit in periodontitis was evaluated by preventive and therapeutic medication in a mouse periodontitis model. Microcomputed tomography, haematoxylin and eosin staining, tartrate-resistant acid phosphatase staining, real-time quantitative polymerase chain reaction, enzyme-linked immunosorbent assay, bead-based immunoassays and flow cytometry were used for histomorphology, molecular biology and cytology analysis. RESULTS: Among different ligands of CCR2, only CCL2 was significantly up-regulated in periodontitis gingival tissues and was positively correlated with the severity of periodontitis. Mice lacking CCL2 showed milder inflammation and less bone resorption than wild-type mice, which was accompanied by a reduction in monocyte/macrophage recruitment. Adeno-associated virus-2 vectors overexpressing CCL2 in Ccl2-/- mice gingiva reversed the attenuation of periodontitis in a CCR2-dependent manner. In ligation-induced experimental periodontitis, preventive or therapeutic administration of bindarit, a CCL2 synthesis inhibitor, significantly inhibited the production of CCL2, decreased the osteoclast number and bone loss and reduced the expression levels of proinflammatory cytokines TNF-α, IL-6 and IL-1ß. CONCLUSIONS: CCL2 is a pivotal chemokine that binds to CCR2 during the progression of periodontitis, and targeting CCL2 may be a feasible option for controlling periodontitis.

Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data.

BACKGROUND: The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. RESULTS: In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. CONCLUSIONS: Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Time-lagged interspecies interactions prevail during biofilm development in moving bed biofilm reactor.

Clarifying the essential succession dynamics of interspecies interactions during biofilm development is crucial for the regulation and application of biofilm-based processes. In this study, regular and time-series phylogenetic molecular ecological networks were constructed to investigate ordinary and time-lagged interspecies interactions during biofilm development in a moving bed biofilm reactor. Positive interactions dominated both regular (89.78%) and time-series (77.04%) ecological networks, suggesting that extensive cooperative behaviors facilitated biofilm development. The pronounced directional interactions (72.52%) in the time-series network further indicated that time-lagged interspecies interactions prevailed in the biofilm development process. Specifically, the proportion of directional negative interactions was higher than that of positive interactions, implying that interspecific competition preferred to be time-lagged. The time-series network revealed that module hubs exhibited extensive time-lagged positive interactions with their neighbors, and most of them exhibited altruistic behaviors. Keystone species possessing more positive interactions were positively correlated with biofilm biomass, NO3 - -N concentrations, and the removal efficiencies of NH4 + -N and chemical oxygen demand. However, keystone species and peripherals that were negatively targeted by their neighbors showed positive correlations with the concentrations of NO2 - -N, polysaccharides, and proteins in the soluble microbial products. The data highlight that the time-series network can provide directional microbial interactions along with the biofilm development process, which would help to predict the tendency of community shifts and propose efficient strategies for the regulation of biofilm-based processes.

Critical roles for CCR2 and the therapeutic potential of cenicriviroc in periodontitis: A pre-clinical study.

AIM: CCR2 plays important roles in many inflammatory and bone metabolic diseases, but its specific role in periodontitis is unknown. In the present study, we aimed to explore the role of CCR2 in the progression of periodontitis and evaluate the effect of cenicriviroc (CVC) on periodontitis. MATERIALS AND METHODS: The expression of CCR2 was studied in patients with periodontitis and in ligation-induced murine model of periodontitis. The role of CCR2 in promoting inflammation and bone resorption in periodontitis was evaluated in Ccr2-/- mice and wild-type mice. The effect of CVC in the prevention and treatment of periodontitis was evaluated by systemic and local medication. Microcomputed tomography, haematoxylin and eosin staining, tartrate-resistant acid phosphatase staining, quantitative real-time polymerase chain reaction, enzyme-linked immunosorbent assay, and flow cytometry were used for histomorphology, molecular biology, and cytology analysis, respectively. RESULTS: In this study, we demonstrated that CCR2 was highly expressed in human and murine periodontitis and that CCR2 deficiency was associated with decreased inflammatory monocyte and macrophage infiltration and inflammatory mediators, osteoclast number and alveolar bone resorption. Prevention and treatment with CVC significantly reduced the severity of periodontitis, regardless of whether it was administered systemically or locally. CONCLUSIONS: CCR2 plays an important role in the development and progression of periodontitis, and CVC is a potential drug for the prevention and treatment of periodontitis.

TaPHT1;9-4B and its transcriptional regulator TaMYB4-7D contribute to phosphate uptake and plant growth in bread wheat.

Efficient phosphate (Pi) uptake and utilisation are essential for promoting crop yield. However, the underlying molecular mechanism is still poorly understood in complex crop species such as hexaploid wheat. Here we report that TaPHT1;9-4B and its transcriptional regulator TaMYB4-7D function in Pi acquisition, translocation and plant growth in bread wheat. TaPHT1;9-4B, a high-affinity Pi transporter highly upregulated in roots by Pi deficiency, was identified using quantitative proteomics. Disruption of TaPHT1;9-4B function by BSMV-VIGS or CRISPR editing impaired wheat tolerance to Pi deprivation, whereas transgenic expression of TaPHT1;9-4B in rice improved Pi uptake and plant growth. Using yeast-one-hybrid assay, we isolated TaMYB4-7D, a R2R3 MYB transcription factor that could activate TaPHT1;9-4B expression by binding to its promoter. Silencing TaMYB4-7D decreased TaPHT1;9-4B expression, Pi uptake and plant growth. Four promoter haplotypes were identified for TaPHT1;9-4B, with Hap3 showing significant positive associations with TaPHT1;9-4B transcript level, growth performance and phosphorus (P) content in wheat plants. A functional marker was therefore developed for tagging Hap3. Collectively, our data shed new light on the molecular mechanism controlling Pi acquisition and utilisation in bread wheat. TaPHT1;9-4B and TaMYB4-7D may aid further research towards the development of P efficient crop cultivars.

Overlooked Ecological Roles of Influent Wastewater Microflora in Improving Biological Phosphorus Removal in an Anoxic/Aerobic MBR Process.

The ecological roles of influent microflora in activated sludge communities have not been well investigated. Herein, parallel lab-scale anoxic/aerobic (A/O) membrane bioreactors (MBRs), which were fed with raw (MBR-C) and sterilized (MBR-T) municipal wastewater, were operated. The MBRs showed comparable nitrogen removal but superior phosphorus removal in MBR-C than MBR-T over the long-term operation. The MBR-C sludge community had higher diversity and deterministic assembly than the MBR-T sludge community as revealed by 16S rRNA gene sequencing and null model analysis. Moreover, the MBR-C sludge community had higher abundance of polyphosphate accumulating organisms (PAOs) and hydrolytic/fermentative bacteria (HFB) but lower abundance of glycogen-accumulating organisms (GAOs), in comparison with MBR-T sludge. Intriguingly, the results of both the net growth rate and Sloan's neutral model demonstrated that HFB in the sludge community were generally slow-growing or nongrowing and their consistent presence in activated sludge was primarily attributed to the HFB immigration from influent microflora. Positive correlations between PAOs and HFB and potential competitions between HFB and GAOs were observed, as revealed by the putative species-species associations in the ecological networks. Taken together, this work deciphers the positive ecological roles of influent microflora, particularly HFB, in system functioning and highlights the necessity of incorporating influent microbiota for the design and modeling of A/O MBR plants.

Ecological Linkages between a Biofilm Ecosystem and Reactor Performance: The Specificity of Biofilm Development Phases.

In biofilm-based engineered ecosystems, the reactor performance was closely linked to interspecies interactions within a biofilm ecosystem, whereas the ecological processes underpinning such linkage were still unenlightened. Herein, the principles of community succession and assembly were integrated to capture the ecological laws of biofilm development by molecular ecological networks and assembly model analysis based on the 16S rRNA sequencing analysis and metagenomics in a well-controlled moving bed biofilm reactor. At the initial colonization phase (days 0-2, driven by initial colonizers), interspecific cooperation (74.18%) facilitated initial biofilm formation, whereas some pioneers, and keystone species disappeared at later phases. At the accumulation phase (days 3-30, rapid biofilm development), interspecific cooperation (81.41 ± 5.07%) contributed to rapid biofilm development and keystone species were mainly involved in quorum sensing or positively correlated with extracellular polymeric substance production. At the maturation phase (days 31-106, a well-adapted quasi-equilibrium state), increased interspecific competition (32.74 ± 4.77%) and higher small-world property facilitated the rapid information transportation and pollutant treatment, and keystone species were positively correlated with the removal of COD and NH4+-N. Homogenizing dispersal diminished the contemporary community dissimilarities, while turnover but rather nestedness governed the temporal variations in the biofilm succession period. This study highlighted the specificity of ecological processes at distinct biofilm development phases, which would advance our understanding on the development-to-function linkages in biofilm-based treatment processes.

Coexistence of Candida albicans and Enterococcus faecalis increases biofilm virulence and periapical lesions in rats.

The present study utilized an in vitro dual-species biofilm model and an in vivo rat post-treatment endodontic disease (PTED) model to investigate whether co-infection of Candida albicans and Enterococcus faecalis would aggravate periapical lesions. The results showed that co-culturing yielded a thicker and denser biofilm more tolerant to detrimental stresses compared with the mono-species biofilm, such as a starvation-alkalinity environment, mechanical shear force and bactericidal chemicals. Consistently, co-inoculation of E. faecalis and C. albicans significantly increased the extent of in vivo periapical lesions compared with mono-species infection. Specifically, coexistence of both microorganisms increased osteoclastic bone resorption and suppressed osteoblastic bone formation. The synergistic effects also up-regulated inflammatory cytokines including TNF-α and IL-6. In summary, coexistence of C. albicans and E. faecalis increased periapical lesions by enhanced biofilm virulence.

IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method.

BACKGROUND: It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. RESULTS: In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. CONCLUSIONS: Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM .

Ecological insights into the underlying evolutionary patterns of biofilm formation from biological wastewater treatment systems: Red or Black Queen Hypothesis?

Interspecies interactions and phylogenetic distances were studied to reveal the underlying evolutionary adaptations of biofilms sourced from wastewater treatment processes. Based on 380 pairwise cocultures of 40 strains from two microbial aggregates (surface-attached and mobile aggregates [flocs]) at two substrate concentrations (LB broth and 0.1× LB broth), interspecies interactions were explored using biofilm classification schemes. There was a strong source-dependence of biofilm development formed by the monocultures, that is, a higher biofilm formation potential for strains from attached aggregates than for those from sludge flocs at both substrate concentrations. Interestingly, the results showed that total biofilm reduction was dominant in the dual-species biofilm sourced from flocs in both LB broth (67.37%) and 0.1× LB broth (64.21%), indicating high interspecific competition in mobile aggregates and the independence of substrate concentrations. However, biofilm reduction was higher (33.68%) than induction (19.37%) for the biofilms formed by surface-attached aggregates in LB broth, while the opposite trend was apparent in 0.1× LB broth, suggesting the occurrence of indeterministic processes for biofilm formation and important roles of substrate concentrations. In addition, the more closely related phylogenetic relationships of cocultures from mobile aggregates were consistent with higher competition compared with those from surface-attached aggregates. Overall, the underlying evolutionary patterns of biofilms formed from mobile aggregates consistently followed the essence of the "Red Queen Hypothesis," while biofilms developed from surface-attached aggregates were not deterministic. This study advanced our understanding of biofilm-related treatment processes using the principles of microbial ecology.

PSO-CFDP: A Particle Swarm Optimization-Based Automatic Density Peaks Clustering Method for Cancer Subtyping.

Cancer subtyping is of great importance for the prediction, diagnosis, and precise treatment of cancer patients. Many clustering methods have been proposed for cancer subtyping. In 2014, a clustering algorithm named Clustering by Fast Search and Find of Density Peaks (CFDP) was proposed and published in Science, which has been applied to cancer subtyping and achieved attractive results. However, CFDP requires to set two key parameters (cluster centers and cutoff distance) manually, while their optimal values are difficult to be determined. To overcome this limitation, an automatic clustering method named PSO-CFDP is proposed in this paper, in which cluster centers and cutoff distance are automatically determined by running an improved particle swarm optimization (PSO) algorithm multiple times. Experiments using PSO-CFDP, as well as LR-CFDP, STClu, CH-CCFDAC, and CFDP, were performed on four benchmark data-sets and two real cancer gene expression datasets. The results show that PSO-CFDP can determine cluster centers and cutoff distance automatically within controllable time/cost and, therefore, improve the accuracy of cancer subtyping.

Assessing perceived quality of primary care under hospital-township health centre integration: A cross-sectional study in China.

Since the new round of health care reform in 2009, the vertical integration of hospitals and primary health institutions has become widely implemented in China as an efficient method for improving quality of primary care. This study aimed to answer the following questions: (a) What is the perceived quality of township health centres (THCs) under integration? (B) What differences could be observed among the three typical integration models, namely, private hospital-THC integration, public hospital-THC integration, and loose collaboration? Two rounds of cross-sectional surveys were conducted from November 2016 to June 2018. The Chinese version of the Primary Care Assessment Tool was used to evaluate perceived quality of sample THCs, and 1118 adult patients were interviewed in total. Multiple linear regressions were employed to compare the quality scores between two survey rounds and among different integration models after controlling for potential confounders. The results revealed that the quality of care significantly improved under private hospital-THC integration as observed by comparing two survey rounds, while no change or slight changes were observed in the other two models. The difference observed among the three models was that the perceived quality of THCs integrated with private hospitals was worse than that of THCs integrated with public hospitals and THCs under loose collaboration, while no significant difference was observed between public hospital-THC integration and loose collaboration. Increased attention should be given to highlighting the tight integration between hospitals and THCs and the different roles played by private and public hospitals in the current reform.

L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions.

BACKGROUND: Predicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction. RESULTS: Since the datasets are usually located at or near low-dimensional nonlinear manifolds, we propose an improved GRMF (graph regularized matrix factorization) method to learn these flow patterns in combination with the previous matrix-decomposition method. In addition, we use one of the pre-processing steps previously proposed to improve the accuracy of the prediction. CONCLUSIONS: Cross-validation is used to evaluate our method, and simulation experiments are used to predict new interactions. In most cases, our method is superior to other methods. Finally, some examples of new drugs and new targets are predicted by performing simulation experiments. And the improved GRMF method can better predict the remaining drug-target interactions.

PDIL1-2 can indirectly and negatively regulate expression of the AGPL1 gene in bread wheat.

BACKGROUND: ADP-glucose pyrophosphorylase (AGPase), the key enzyme in plant starch biosynthesis, is a heterotetramer composed of two identical large subunits and two identical small subunits. AGPase has plastidial and cytosolic isoforms in higher plants, whereas it is mainly detected in the cytosol of grain endosperms in cereal crops. Our previous results have shown that the expression of the TaAGPL1 gene, encoding the cytosolic large subunit of wheat AGPase, temporally coincides with the rate of starch accumulation and that its overexpression dramatically increases wheat AGPase activity and the rate of starch accumulation, suggesting an important role. METHODS: In this study, we performed yeast one-hybrid screening using the promoter of the TaAGPL1 gene as bait and a wheat grain cDNA library as prey to screen out the upstream regulators of TaAGPL1 gene. And the barley stripe mosaic virus-induced gene-silencing (BSMV-VIGS) method was used to verify the functional characterization of the identified regulators in starch biosynthesis. RESULTS: Disulfide isomerase 1-2 protein (TaPDIL1-2) was screened out, and its binding to the TaAGPL1-1D promoter was further verified using another yeast one-hybrid screen. Transiently silenced wheat plants of the TaPDIL1-2 gene were obtained by using BSMV-VIGS method under field conditions. In grains of BSMV-VIGS-TaPDIL1-2-silenced wheat plants, the TaAGPL1 gene transcription levels, grain starch contents, and 1000-kernel weight also significantly increased. CONCLUSIONS: As important chaperones involved in oxidative protein folding, PDIL proteins have been reported to form hetero-dimers with some transcription factors, and thus, our results suggested that TaPDIL1-2 protein could indirectly and negatively regulate the expression of the TaAGPL1 gene and function in starch biosynthesis.