Bisphenol mixtures, metal mixtures and type 2 diabetes mellitus: Insights from metabolite profiling.

BACKGROUND: Little is known about the combined effect of bisphenol mixtures and metal mixtures on type 2 diabetes mellitus (T2DM) risk, and the mediating roles of metabolites. METHODS: The study included 606 pairs of T2DM cases and controls matched by age and sex, and information of participants was collected through questionnaires and laboratory tests. Serum bisphenol and plasma metal concentrations were measured using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. Widely targeted metabolomics was employed to obtain the serum metabolomic profiles. Conditional logistic regression models were used to assess the single associations of bisphenols and metals with T2DM risk after multivariable adjustment. Additionally, the joint effects of bisphenol mixtures and metal mixtures were examined using quantile-based g-computation (QG-C) models. Furthermore, differential metabolites associated with T2DM were identified, and mediation analyses were performed to explore the role of metabolites in the associations of bisphenols and metals with T2DM risk. RESULTS: The results showed bisphenol mixtures were associated with an increased T2DM risk, with bisphenol A (BPA) identified as the primary contributor. While the association between metal mixtures and T2DM remained inconclusive, cobalt (Co), iron (Fe), and zinc (Zn) showed the highest weight indices for T2DM risk. A total of 154 differential metabolites were screened between the T2DM cases and controls. Mediation analyses indicated that 9 metabolites mediated the association between BPA and T2DM, while L-valine mediated the association between Zn and T2DM risk. CONCLUSIONS: The study indicated that BPA, Co, Fe, and Zn were the primary contributors to increased T2DM risk, and metabolites played a mediating role in the associations of BPA and Zn with the risk of T2DM. Our findings contribute to a better understanding of the mechanisms underlying the associations of bisphenols and metals with T2DM.

Life cycle assessment of biochar for sustainable agricultural application: A review.

Biochar application is an effective strategy to address Agro-climatic challenges. However, the agro-environmental impacts of different biochar technology models are lacking of systematic summaries and reviews. Therefore, this paper comprehensively reviews recent developments derived from published literature, delving into the economic implications and environmental benefits of three distinct process namely technologies-pyrolysis, gasification, and hydrothermal carbonization. This paper specifically focuses on the agricultural life cycle assessment (LCA) methodology, and the influence of biochar preparation technologies and products on energy consumption and agricultural carbon emissions. LCA analysis shows that process and feedstock pose a predominant role on the properties and production rate of biochar, while gasification technology exhibits excellent economic attributes compared to the other two technologies. Biochar applications in agricultural has the beneficial effect of sequestering carbon and reducing emissions, especially in the area of mitigating the carbon footprint of farmland. However, the complexity of the composition of the prepared feedstock and the mismatch between the biochar properties and the application scenarios are considered as potential sources of risks. Notably, mechanism of carbon sequestration and emission reduction by soil microorganisms and agro-environmental sequestration by biochar application remains unclear, calling for in-depth studies. We review novel aspects that have not been covered by previous reviews by comparing the technical, economic, and environmental benefits of pyrolysis, gasification, and hydrothermal carbonization systematically. Overall, this study will provide a valuable framework to environmental implications of biochar preparation, application, and life cycle assessments.

Prognostic factors of patients with human epidermal growth factor receptor 2-positive breast cancer following neoadjuvant therapy: Development and validation of a predictive nomogram.

OBJECTIVE: Human epidermal growth factor receptor 2 (HER2)-positive breast cancer exhibits an aggressive phenotype and poor prognosis. The application of neoadjuvant therapy (NAT) in patients with breast cancer can significantly reduce the risks of disease recurrence and improve survival. By integrating different clinicopathological factors, nomograms are valuable tools for prognosis prediction. This study aimed to assess the prognostic value of clinicopathological factors in patients with HER2-positive breast cancer and construct a nomogram for outcome prediction. METHODS: We retrospectively analyzed the clinicopathological data from 374 patients with breast cancer admitted to the Fourth Hospital of Hebei Medical University between January 2009 and December 2017, who were diagnosed with invasive breast cancer through preoperative core needle biopsy pathology, underwent surgical resection after NAT, and were HER2-positive. Patients were randomly divided into a training and validation set at a ratio of 7:3. Univariate and multivariate survival analyses were performed using Kaplan-Meier and Cox proportional hazards regression models. Results of the multivariate analysis were used to create nomograms predicting 3-, 5-, and 8-year overall survival (OS) rates. Calibration curves were plotted to test concordance between the predicted and actual risks. Harrell C-index and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discriminability of the nomogram prediction model. RESULTS: All included patients were women, with a mean age of 50 ± 10.4 years (range: 26-72 years). In the training set, both univariate and multivariate analyses identified residual cancer burden (RCB) class, tumor-infiltrating lymphocytes(TILs), and clinical stage as independent prognostic factors for OS, and these factors were combined to construct a nomogram. The calibration curves demonstrated good concordance between the predicted and actual risks, and the C-index of the nomogram was 0.882 (95 % CI 0.863-0.901). The 3-, 5-, and 8-year areas under the ROC curve (AUCs) were 0.909, 0.893, and 0.918, respectively, indicating good accuracy of the nomogram. The calibration curves also demonstrated good concordance in the validation set, with a C-index of 0.850 (95 % CI 0.804-0.896) and 3-, 5-, and 8-year AUCs of 0.909, 0.815, and 0.834, respectively, which also indicated good accuracy. CONCLUSION: The nomogram prediction model accurately predicted the prognostic status of post-NAT patients with breast cancer and was more accurate than clinical stage and RCB class. Therefore, it can serve as a reliable guide for selecting clinical treatment measures for breast cancer.

Effect of different extraction methods on the lipid composition and antioxidant activity of Ziziphi spinosae semens oil.

Chinese sour jujube is an important fruit for both medicine and food and effects various biological activities. Although the Chinese sour jujube seed (Ziziphi spinosae semen) is rich in oil, its lipid profiles is significantly affected by the extraction process. However, no studies to date have comprehensively analysed the lipid composition and bioactivity of Ziziphi spinosae semen oil processed using different methods. In this study, we compared the effects of commonly-used pressed, ultrasonic-assisted, and Soxhlet extraction methods on the lipid composition, characteristics, and antioxidant properties of Ziziphi spinosae semen oil. Nineteen subclasses and 390 lipid molecular species were identified, of which 24 lipid molecular species could potentially be used as biomarkers for different processing methods. Correlation analysis revealed that 57 lipids were significantly correlated with the antioxidant capacity (r > 0.9 and P < 0.05). These results indicate that Ziziphi spinosae semen oil is rich in bioactive lipids. These data greatly expand our understanding of the bioactive lipids of Ziziphi spinosae semen oil. Additionally, it could provide useful information for Ziziphi spinosae semen oil applications in functional products or the food industry and new insights into the effects of active vegetable oil processing.

Anaerobic dynamic membrane bioreactor for the co-digestion of toilet blackwater and kitchen waste.

Anaerobic co-digestion using an anaerobic dynamic membrane bioreactor (AnDMBR) can separate the sludge retention time and hydraulic retention time, retaining the biomass for efficient degradation and the use of less expensive large pore-size membrane materials and more sustainable dynamic membranes (DMs). Therefore, anaerobic co-digestion of toilet blackwater (BW) and kitchen waste (KW) using an AnDMBR was hypothesized to increase the potential for co-digestion. Here, the efficiency and stability of AnDMBR in anaerobic co-digestion of toilet BW and KW were investigated. DM morphology and structural characteristics, filtration properties, and composition, as well as membrane contamination and membrane regeneration mechanisms, were investigated. Average daily biogas yields of the reactor in two membrane cycles before and after cleaning were 788.67 and 746.09 ml/g volatile solids, with average methane content of 66.64% and 67.27% and average COD removal efficiencies of 82.03% and 80.96%, respectively. The results showed that the bioreactor obtained good performance and stability. During the stabilization phase of the DM operation, the flux was maintained between 43.65 and 65.15 L/m2/h. DM was mainly composed of organic and inorganic elements. Off-line cleaning facilitated DM regulation and regeneration, restoring new Anaerobic morphology and structure. PRACTITIONER POINTS: High efficiency co-digestion of BW and KW was realized in the DMBR system. Average daily biogas yields before and after membrane cleaning were 788.67 and 746.09 ml/g volatile solids. Off-line cleaning facilitated DM regulation and regeneration as well as system stability. The flux was maintained between 43.65 and 65.15 L/m2/h during operation.

Re-establishing immune tolerance in multiple sclerosis: focusing on novel mechanisms of mesenchymal stem cell regulation of Th17/Treg balance.

The T-helper 17 (Th17) cell and regulatory T cell (Treg) axis plays a crucial role in the development of multiple sclerosis (MS), which is regarded as an immune imbalance between pro-inflammatory cytokines and the maintenance of immune tolerance. Mesenchymal stem cell (MSC)-mediated therapies have received increasing attention in MS research. In MS and its animal model experimental autoimmune encephalomyelitis, MSC injection was shown to alter the differentiation of CD4+T cells. This alteration occurred by inducing anergy and reduction in the number of Th17 cells, stimulating the polarization of antigen-specific Treg to reverse the imbalance of the Th17/Treg axis, reducing the inflammatory cascade response and demyelination, and restoring an overall state of immune tolerance. In this review, we summarize the mechanisms by which MSCs regulate the balance between Th17 cells and Tregs, including extracellular vesicles, mitochondrial transfer, metabolic reprogramming, and autophagy. We aimed to identify new targets for MS treatment using cellular therapy by analyzing MSC-mediated Th17-to-Treg polarization.

O-Glycosyltransferase Gene BnaC09.OGT Involved in Regulation of Unsaturated Fatty Acid Biosynthesis for Enhancing Osmotic Stress Tolerance in Brassica napus L.

Osmotic stress is a major threaten to the growth and yield stability of Brassica napus. Post-translational modification with O-linked ß-N-acetylglucosamine (O-GlcNAc) is ubiquitous in plants, and participates in a variety of signal transduction and metabolic regulation. However, studies on the role of O-GlcNAc transferase (OGT) in osmotic stress tolerance of plants are limited. In previous study, a O-glycosyltransferase, named BnaC09.OGT, was identified from the B. napus variety 'Zhongshuang 11' by yeast one hybrid with promoter of BnaA01.GPAT9. It was found that BnaC09.OGT localized in both nucleus and cytoplasm. The spatiotemporal expression pattern of BnaC09.OGT exhibited tissue specificity in developmental seed, especially in 15 days after pollination. In view of osmotic stress inducing, the BnaC09.OGT overexpression and knockout transgenic lines were constructed for biological function study. Phenotypic analysis of BnaC09.OGT overexpression seedlings demonstrated that BnaC09.OGT could enhance osmotic stress tolerance than WT and knockout lines in euphylla stage under 15% PEG6000 treatment after 7 days. In addition, compared with WT and knockout lines, overexpression of BnaC09.OGT had significantly higher activities of antioxidant enzymes (SOD and POD), higher content of soluble saccharide, and while significantly less content of malondialdehyde, proline and anthocyanidin under 15% PEG6000 treatment after 7 days. On the other hand, the unsaturated fatty acid content of BnaC09.OGT overexpression was significantly higher than that of WT and knockout lines, so it is speculated that the BnaC09.OGT could increase unsaturated fatty acid biosynthesis for osmotic stress tolerance by promoting the expression of BnaA01.GPAT9 in glycerolipid biosynthesis. In summary, the above results revealed that the function of BnaC09.OGT provides new insight for the analysis of the pathway of O-glycosylation in regulating osmotic stress tolerance in B. napus.

Genetic Engineering for Enhancing Sugarcane Tolerance to Biotic and Abiotic Stresses.

Sugarcane, a vital cash crop, contributes significantly to the world's sugar supply and raw materials for biofuel production, playing a significant role in the global sugar industry. However, sustainable productivity is severely hampered by biotic and abiotic stressors. Genetic engineering has been used to transfer useful genes into sugarcane plants to improve desirable traits and has emerged as a basic and applied research method to maintain growth and productivity under different adverse environmental conditions. However, the use of transgenic approaches remains contentious and requires rigorous experimental methods to address biosafety challenges. Clustered regularly interspaced short palindromic repeat (CRISPR) mediated genome editing technology is growing rapidly and may revolutionize sugarcane production. This review aims to explore innovative genetic engineering techniques and their successful application in developing sugarcane cultivars with enhanced resistance to biotic and abiotic stresses to produce superior sugarcane cultivars.

Genomic and Phenotypic Analysis of bla KPC-2 Associated Carbapenem Resistance in Klebsiella aerogenes: Insights into Clonal Spread and Resistance Mechanisms Across Hospital Departments in Beijing.

Purpose: This study conducted an phenotypic and whole-genome sequencing analysis with Klebsiella aerogenes to elucidate its clinical epidemiological characteristics, antimicrobial resistance (AMR) phenotype, biofilm formation ability and hemolytic activity testing, AMR genes and phylogenetic relationships, so as to provide a further understanding of the intra-hospital strain transmission. Methods: Samples were collected from a hospital in Beijing between 2020 and 2022. All strains underwent bacterial identification, antimicrobial susceptibility testing (AST) using the VITEK-2 compact system. Biofilm formation ability and hemolytic activity were tested. Second-generation sequencing was applied to all strains, with those carrying the bla KPC gene were selected for third-generation sequencing. Whole-genome analysis identified resistance genes, plasmid types, MLST typing, and phylogenetic relationships. Plasmids were assembled to detect plasmid structures and AMR gene location. Results: Among the 42 K. aerogenes isolates, 21 were carbapenem-resistant K. aerogenes (CRKA). All strains exhibited strong biofilm formation and no hemolytic activity. Most were sourced from sputum (83.3%). CRKA demonstrated extensive resistance to antibiotics, particularly ß-lactamase inhibitors and Cefotetan. This resistance pattern was closely associated with the presence of an IncFII(pHN7A8) plasmid, which carried multiple resistance genes, including bla KPC-2, bla CTX-M-65, bla TEM-1, rmtB and a large number of mobile elements. The majority of CRKA strains clustered within the same branch of the phylogenetic tree, exhibiting minimal single nucleotide polymorphism (0-13 SNPs) differences, and they shared the same sequence type (ST292), resistance genes, and plasmids, originating from different departments, suggesting clonal transmission among the hospital. Conclusion: Our research reveals that the clonal transmission of CRKA occurs across various departments within the hospital. The widespread resistance observed in CRKA, attributed to the presence of bla KPC and ESBLs genes, underscores the need for heightened vigilance to prevent the further dissemination of CRKA within the hospital and, potentially, throughout the wider community.

Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression.

Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

Characterization of a Salmonella enterica serovar Typhimurium lineage with rough colony morphology and multidrug resistance.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a major cause of salmonellosis, and the emergence of multidrug-resistant pathovariants has become a growing concern. Here, we investigate a distinct rough colony variant exhibiting a strong biofilm-forming ability isolated in China. Whole-genome sequencing on 2,212 Chinese isolates and 1,739 publicly available genomes reveals the population structure and evolutionary history of the rough colony variants. Characterized by macro, red, dry, and rough (mrdar) colonies, these variants demonstrate enhanced biofilm formation at 28 °C and 37 °C compared to typical rdar colonies. The mrdar variants exhibit extensive multidrug resistance, with significantly higher resistance to at least five classes of antimicrobial agents compared to non-mrdar variants. This resistance is primarily conferred by an IncHI2 plasmid harboring 19 antimicrobial resistance genes. Phylogenomic analysis divides the global collections into six lineages. The majority of mrdar variants belong to sublineage L6.5, which originated from Chinese smooth colony strains and possibly emerged circa 1977. Among the mrdar variants, upregulation of the csgDEFG operons is observed, probably due to a distinct point mutation (-44G > T) in the csgD gene promoter. Pangenome and genome-wide association analyses identify 87 specific accessory genes and 72 distinct single nucleotide polymorphisms associated with the mrdar morphotype.

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis.

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less objective than automated, computer-based evaluations. Artificial Intelligence (AI) promises enhanced accuracy and reproducibility in HER2 interpretation. This study aimed to systematically evaluate current AI algorithms for HER2 immunohistochemical diagnosis, offering insights to guide the development of more adaptable algorithms in response to evolving HER2 assessment practices. A comprehensive data search of the PubMed, Embase, Cochrane, and Web of Science databases was conducted using a combination of subject terms and free text. A total of 4994 computational pathology articles published from inception to September 2023 identifying HER2 expression in breast cancer were retrieved. After applying predefined inclusion and exclusion criteria, seven studies were selected. These seven studies comprised 6867 HER2 identification tasks, with two studies employing the HER2-CONNECT algorithm, two using the CNN algorithm, one with the multi-class logistic regression algorithm, and two using the HER2 4B5 algorithm. AI's sensitivity and specificity for distinguishing HER2 0/1+ were 0.98 [0.92-0.99] and 0.92 [0.80-0.97] respectively. For distinguishing HER2 2+, the sensitivity and specificity were 0.78 [0.50-0.92] and 0.98 [0.93-0.99], respectively. For HER2 3+ distinction, AI exhibited a sensitivity of 0.99 [0.98-1.00] and specificity of 0.99 [0.97-1.00]. Furthermore, due to the lack of HER2-targeted therapies for HER2-negative patients in the past, pathologists may have neglected to distinguish between HER2 0 and 1+, leaving room for improvement in the performance of artificial intelligence (AI) in this differentiation. AI excels in automating the assessment of HER2 immunohistochemistry, showing promising results despite slight variations in performance across different HER2 status. While incorporating AI algorithms into the pathology workflow for HER2 assessment poses challenges in standardization, application patterns, and ethical considerations, ongoing advancements suggest its potential as a widely effective tool for pathologists in clinical practice in the near future.

Tumour mutation burden and infiltrating immune cell subtypes influenced the breast cancer prognosis.

Background: Most of its issues are still undecided on the relationship between tumour mutation burden (TMB) and immune-related genes in the breast cancer. This study explores their relationship based on gene mutation and transcription data in The Cancer Genome Atlas (TCGA) database, and the effects of immune cells in TMB and tumour microenvironments on prognosis of breast cancer patients. Methods: Cases were divided into low-TMB and high-TMB subgroups. Differentially expressed immune-related genes were identified in different TMB subgroups, and patient prognosis was predicted by gene function enrichment analysis, invasive immune cells and different clinical pathological features were compared among different TMB subgroups. Results: A total of 986 mutation data from breast cancer patients were obtained. Compared with low-TMB group, the survival period of high-TMB group was relatively longer. A total of 337 differential expression genes were identified in this study. Of these genes, seven differentially expressed immune-related genes were associated with prognosis. In the high-TMB group, activated CD4+ memory T cells and other cells had high expression, the expression ratio of memory B cells and other cells in low-TMB group was high. Conclusions: TMB-related immunological infiltration characteristics showed meaningful value for prognosis prediction for breast cancer patients. Differentially expressed immune-related genes in TMB subgroups provide important information on the survival prediction.

Copper induces cytotoxicity in freshwater bivalve Anodonta woodiana hemocytes.

Hemocytes of freshwater bivalves are an important target model for evaluating copper (Cu) toxicity in vitro, with excess Cu causing adverse responses in these organisms. Despite this, the mechanisms underlying cytotoxicity remain poorly understood. The freshwater bivalve Anodonta woodiana, employed as a model organism in freshwater environments, was utilized in this study. Hemocytes of A. woodiana were exposed to various aqueous Cu treatments (0.001, 0.01, 0.1, 1, and 10 mg/L), and a control group (no Cu added) for 3 h to investigate the cytotoxic mechanisms of Cu. The results showed a significant increase in the production of reactive oxygen species in hemocytes of all Cu exposed groups compared to the control (p < 0.05). Remarkably, Cu treatments disrupted the cellular membrane (p < 0.05) but did not induce significant changes in the stability of the lysosomal membrane. Cu targeted the mitochondria, leading to a reduction in mitochondrial membrane potential. Additionally, all Cu treatments significantly increased the degree of DNA damage (p < 0.05). Cellular damage and a significant decline in cell viability were observed when the Cu exposure concentration reached 0.1, 1, and 10 mg/L (p < 0.05). Our study provides new insights into the cytotoxicity mechanisms triggered by Cu in hemocytes of the freshwater bivalve A. woodiana, even under environmentally relevant conditions of 0.01 mg/L exposure.

Far-field superresolution of thermal sources by double homodyne or double array homodyne detection.

We propose two schemes for estimating the separation of two thermal sources via double homodyne and double array homodyne detection considering the joint measurement of conjugate quadratures of the image plane field.By using the Cramér-Rao bound, we demonstrate that the two schemes can estimate the separation well below the Rayleigh limit and have an advantage over direct imaging when the average photon number per source exceeds five.For arbitrary source strengths, double homodyne detection is superior to homodyne detection when the separation is above 25/4 σ/N s , σ is the beam width, Ns is the average photon number per source.A larger separation can be estimated better via double array homodyne detection with the superiority of flexible operation compared with other schemes. High-speed and high-efficiency detection enables the measurement schemes with potential practical applications in fluorescence microscopy, astronomy and quantum imaging.

Pixelwise immersion factor calibration for underwater hyperspectral imaging instruments.

In situ spectral reflectance initially captured at high spatial resolution with underwater hyperspectral imaging (UHI) is effective for classification and quantification in oceanic biogeochemical studies; however, the measured spectral radiance is rarely used as an absolute quantity due to challenges in calibration of UHI instruments. In this paper, a commercial UHI instrument was calibrated for radiometric flat field response and pixelwise immersion effect to support in situ measurement of absolute spectral radiance. The radiometric and immersion factor calibrations of the UHI instrument were evaluated quantitatively through comparative experiments with a spectroradiometer and a spectrometer. Results show that the immersion factor of the center pixel of the tested UHI instrument was 1.763 in pure water at 600 nm, and the averaged difference in immersion factor between the center and edge pixel of the UHI instrument in the visible light band was only 1∼3% across its half angle field of view of 35° in air. The new calibration coefficients were further used to calculate the spectral radiance of transmitted sunlight through ice algae clusters in sea ice measured by the UHI instrument during an Arctic under-ice bio-optical survey.

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China.

BACKGROUND: The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations. METHODS: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation. RESULTS: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05. CONCLUSIONS: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Improve-RRBS: a novel tool to correct the 3' trimming of reduced representation sequencing reads.

Motivation: Reduced Representation Bisulfite Sequencing (RRBS) is a popular approach to determine DNA methylation of the CpG-rich regions of the genome. However, we observed that false positive differentially methylated sites (DMS) are also identified using the standard computational analysis. Results: During RRBS library preparation the MspI digested DNA undergo end-repair by a cytosine at the 3' end of the fragments. After sequencing, Trim Galore cuts these end-repaired nucleotides. However, Trim Galore fails to detect end-repair when it overlaps with the 3' end of the sequencing reads. We found that these non-trimmed cytosines bias methylation calling, thus, can identify DMS erroneously. To circumvent this problem, we developed improve-RRBS, which efficiently identifies and hides these cytosines from methylation calling with a false positive rate of maximum 0.5%. To test improve-RRBS, we investigated four datasets from four laboratories and two different species. We found non-trimmed 3' cytosines in all datasets analyzed and as much as >50% of false positive DMS under certain conditions. By applying improve-RRBS, these DMS completely disappeared from all comparisons. Availability and implementation: Improve-RRBS is a freely available python package https://pypi.org/project/iRRBS/ or https://github.com/fothia/improve-RRBS to be implemented in RRBS pipelines.

Association of metals and bisphenols exposure with lipid profiles and dyslipidemia in Chinese adults: Independent, combined and interactive effects.

BACKGROUND: Although studies have assessed the association of metals and bisphenols with lipid metabolism, the observed results have been controversial, and limited knowledge exists about the combined and interactive effects of metals and bisphenols exposure on lipid metabolism. METHODS: Plasma metals and serum bisphenols concentrations were evaluated in 888 participants. Multiple linear regression and logistic regression models were conducted to assess individual associations of 18 metals and 3 bisphenols with 5 lipid profiles and dyslipidemia risk, respectively. The dose-response relationships of targeted contaminants with lipid profiles and dyslipidemia risk were captured by applying a restriction cubic spline (RCS) function. The bayesian kernel machine regression (BKMR) model was used to assess the overall effects of metals and bisphenols mixture on lipid profiles and dyslipidemia risk. The interactive effects of targeted contaminants on interested outcomes were explored by constructing an interaction model. RESULTS: Single-contaminant analyses revealed that exposure to iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), strontium (Sr), and tin (Sn) was associated with elevated lipid levels. Cobalt (Co) showed a negative association with high density lipoprotein cholesterol (HDL-C). Bisphenol A (BPA) and bisphenol AF (BPAF) were associated with decreased HDL-C levels, with nonlinear associations observed. Vanadium (V), lead (Pb), and silver (Ag) displayed U-shaped dose-response relationships with most lipid profiles. Multi-contaminant analyses indicated positive trends between contaminants mixture and total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). The interaction analyses showed that Se-Fe exhibited synergistic effects on LDL-C and non-HDL-C, and Se-Sn showed a synergistic effect on HDL-C. CONCLUSIONS: Our study suggested that exposure to metals and bisphenols was associated with changes in lipid levels, and demonstrated their combined and interactive effects.

Inhibition of the ATP synthase increases sensitivity of Escherichia coli carrying mcr-1 to polymyxin B.

Bacterial infections caused by multidrug-resistant (MDR) gram-negative strains carrying the mobile colistin resistance gene mcr-1 are serious threats to world public health due to the lack of effective treatments. Inhibition of the ATP synthase makes bacteria such as Staphylococcus aureus and Klebsiella pneumoniae more sensitive to polymyxin. This provides new strategies for treating infections caused by polymyxins-resistant bacteria carrying mcr-1. Six mcr-1-positive strains were isolated from clinical samples, and all were identified as Escherichia coli. Here we investigated several ATP synthase inhibitors, N,N'-dicyclohexylcarbodiimide (DCCD), resveratrol, and piceatannol, for their antibacterial effects against the mcr-1-positive strains combined with polymyxin B (POL). Checkerboard assay, time-kill assay, biofilm inhibition and eradication assay indicated the significant synergistic effect of ATP synthase inhibitors/POL combination in vitro. Meanwhile, mouse infection model experiment was also performed, showing a 5 log10 reduction of the pathogen after treatment with the resveratrol/POL combination. Moreover, adding adenosine disodium triphosphate (Na2ATP) could inhibit the antibacterial effect of the ATP synthase inhibitors/POL combination. In conclusion, our study confirmed that inhibition of ATP production could increase the susceptibility of bacteria carrying mcr-1 to polymyxins. This provides a new strategy against polymyxins-resistant bacteria infection.