The associations of IGF2, IGF2R and IGF2BP2 gene polymorphisms with gestational diabetes mellitus: A case-control study.

OBJECTIVE: To investigate the associations of Insulin-like growth factor-II (IGF2) gene, Insulin-like growth factor-II receptor (IGF2R) gene and Insulin-like growth factor-II binding protein 2 (IGF2BP2) gene polymorphisms with the susceptibility to gestational diabetes mellitus (GDM) in Chinese population. METHODS: A total of 1703 pregnant women (835 GDM and 868 Non-GDM) were recruited in this case-control study. All participants underwent prenatal 75 g oral glucose tolerance test (OGTT) examinations during 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. Genotyping of candidate SNPs (IGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs1374910, rs11705701, rs6777038, rs16860234, rs7651090) was performed on Sequenom MassARRAY platform. Logistic regression analysis was conducted to investigate the associations between candidate SNPs and risk of GDM. In addition, multifactor dimensionality reduction (MDR) method was applied to explore the effects of gene-gene interactions on GDM risk. RESULTS: There were significant distribution differences between GDM group and non-GDM group in age, pre-pregnancy BMI, education level and family history of diabetes (P < 0.05). After adjusted for age, pre-pregnancy BMI, education level and family history of diabetes, there were no significant associations of the candidate SNPs polymorphisms and GDM risk (P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the candidate SNPs (P > 0.05). However, the fasting blood glucose (FBG) levels of rs6777038 CT carriers were significantly lower than TT carriers (4.69±0.69 vs. 5.03±1.57 mmol/L, P < 0.01), and the OGTT-2h levels of rs6777038 CC and CT genotype carriers were significantly lower than TT genotype carriers (8.10±1.91 and 8.08±1.87 vs. 8.99±2.90 mmol/L, P < 0.01). CONCLUSIONS: IGF2 rs680, IGF2R rs416572, IGF2BP2 rs4402960, rs1470579, rs11705701, rs6777038, rs16860234, rs7651090 polymorphisms were not significantly associated with GDM risk in Wuhan, China. Further lager multicenter researches are needed to confirm these results.

Tumor Microenvironment-Responsive Polymer-Based RNA Delivery Systems for Cancer Treatment.

Ribonucleic acid (RNA) therapeutics offer a broad prospect in cancer treatment. However, their successful application requires overcoming various physiological barriers to effectively deliver RNAs to the target sites. Currently, a number of RNA delivery systems based on polymeric nanoparticles are developed to overcome these barriers in RNA delivery. This work provides an overview of the existing RNA therapeutics for cancer gene therapy, and particularly summarizes those that are entering the clinical phase. This work then discusses the core features and latest research developments of tumor microenvironment-responsive polymer-based RNA delivery carriers which are designed based on the pathological characteristics of the tumor microenvironment. Finally, this work also proposes opportunities for the transformation of RNA therapies into cancer immunotherapy methods in clinical applications.

Reinforced GNNs for Multiple Instance Learning.

Multiple instance learning (MIL) trains models from bags of instances, where each bag contains multiple instances, and only bag-level labels are available for supervision. The application of graph neural networks (GNNs) in capturing intrabag topology effectively improves MIL. Existing GNNs usually require filtering low-confidence edges among instances and adapting graph neural architectures to new bag structures. However, such asynchronous adjustments to structure and architecture are tedious and ignore their correlations. To tackle these issues, we propose a reinforced GNN framework for MIL (RGMIL), pioneering the exploitation of multiagent deep reinforcement learning (MADRL) in MIL tasks. MADRL enables the flexible definition or extension of factors that influence bag graphs or GNNs and provides synchronous control over them. Moreover, MADRL explores structure-to-architecture correlations while automating adjustments. Experimental results on multiple MIL datasets demonstrate that RGMIL achieves the best performance with excellent explainability. The code and data are available at https://github.com/RingBDStack/RGMIL.

Intraoperative central venous pressure during cardiopulmonary bypass is an alternative indicator for early prediction of acute kidney injury in adult cardiac surgery.

BACKGROUND: The relationship between venous congestion in cardiopulmonary bypass (CPB) and acute kidney injury (AKI) in cardiac surgery has not utterly substantiated. This study aimed at investigate the relationship between CVP in CPB and the occurrence of AKI. METHODS: We retrospectively reviewed 2048 consecutive patients with cardiovascular disease undergoing cardiac procedure with CPB from January 2018 to December 2022. We used the median CVP value obtained during CPB for our analysis and patients were grouped according to this parameter. The primary outcomes were AKI and renal replacement therapy(RRT). Multivariable logistic regression was used to explore the association between CVP and AKI. RESULTS: A total of 2048 patients were enrolled in our study and divided into high CVP group (CVP ≥ 6.5 mmHg) and low CVP group (CVP < 6.5 mmHg) according to the median CVP value. Patients in high CVP group had the high AKI and RRT rate when compared to the low CVPgroup[(367/912,40.24%)vs.(408/1136,35.92%),P = 0.045;(16/912,1.75%vs.9/1136;0.79%), P = 0.049]. Multivariate logistic regression analysis displayed CVP played an indispensable part in development of renal failure in surgical. CONCLUSIONS: Elevated CVP(≥ 6.5mmH2OmmHg) in CPB during cardiac operation is associated with an increased risk of AKI in cardiovascular surgery patients. Clinical attention should be paid to the potential role of CVP in predicting the occurrence of AKI.

The effect of grape seed procyanidins extract on cognitive function in elderly people with mild cognitive impairment: A randomized, double-blind, placebo-controlled clinical trial.

Background: Procyanidins have antioxidative properties that may protect against age-related brain oxidative stress. Previous studies indicated that procyanidin-rich foods could improve cognitive function and prevent neurodegenerative diseases. This study hypothesized that grape seed procyanidins extract (GSPE) would have a favorable effect on cognitive function in elderly people with mild cognitive impairment (MCI). Methods: A community-based, randomized, double-blind, placebo-controlled trial was conducted. Participants aged 60 years or older with MCI were randomly assigned into the GSPE group (n = 35, 320 mg/d) or placebo group (n = 36), and received capsules for 6 months. Cognitive function was assessed using the Montreal Cognitive Assessment Scale (MoCA). The change in MoCA scores between groups were tested by the time ✕ treatment interaction in mixed-design ANOVA. Results: After 6 months of intervention, the MoCA score was higher than the baseline both in the intervention group and placebo control group, while the there was no significant difference for mean change in MoCA score from baseline between the intervention group and the placebo group (2.35 ± 3.20 vs. 1.28 ± 2.93, P = 0.192). Conclusions: Present study showed that 6-month supplementation with GSPE did not significantly improve cognitive function in subjects with MCI. Further investigations regarding the longer-term intervention effect of procyanidins extract on mild or moderate cognitive disorders are needed.

Association of the CDKAL1 gene polymorphism with gestational diabetes mellitus in Chinese women.

INTRODUCTION: To identify the association of the cyclin-dependent kinase 5 regulatory subunit associated protein 1-like 1 (CDKAL1) gene polymorphism with gestational diabetes mellitus (GDM) in the Chinese population. RESEARCH DESIGN AND METHODS: This case-control study enrolled 835 pregnant women with GDM and 870 pregnant women without diabetes who underwent antenatal examination during 24 to 28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. Trained nurses collected their clinical information and blood samples. CDKAL1 gene rs10440833, rs10946398, rs4712523, rs4712524, rs7754840, rs7756992 and rs9465871 loci were genotyped by Agena MassARRAY system. SPSS V.26.0 software and online SHesis were used to analyze the relationship between CDKAL1 gene polymorphism and GDM susceptibility. RESULTS: After being adjusted for maternal age, prepregnancy body mass index (BMI), parity and family history of type 2 diabetes mellitus (T2DM), CDKAL1 gene rs10440833 (AA vs TT, OR=1.631, 95% CI 1.192 to 2.232), rs10946398 (CC vs AA, OR=1.400, 95% CI 1.028 to 1.905), rs4712523 (GG vs AA, OR=1.409, 95% CI 1.038 to 1.913), rs4712524 (GG vs AA, OR=1.418, 95% CI 1.043 to 1.929) and rs7754840 (CC vs GG, OR=1.407, 95% CI 1.036 to 1.911) polymorphisms were all associated with the increased risk of GDM. In addition, there was a powerful linkage disequilibrium (LD) among rs10946398, rs4712523, rs4712524 and rs7754840 (D'>0.900, r2>0.900). And there were significant differences in haplotype CGGC (OR=1.207, 95% CI 1.050 to 1.387) and AAAG (OR=0.829, 95% CI 0.721 o 0.952, p=0.008) between the GDM group and the control group. CONCLUSIONS: rs10440833, rs10946398, rs4712523, rs4712524 and rs7754840 of CDKAL1 gene are associated with GDM susceptibility in central Chinese population.

Genetic alterations of GI-NECs involving three main signaling pathways.

BACKGROUND: Gastrointestinal (GI)-neuroendocrine neoplasms (NENs) are subclassified in neuroendocrine tumors (NETs), neuroendocrine carcinomas (NECs), and mixed neuroendocrine-non-neuroendocrine neoplasms (MiNENs). The genetic characteristics of GI-NEN has been a hot issue in recent years, but more studies are needed to provide further details. This study aims to provide additional data about genomic characteristics of GI-NENs and the genetic differences between NETs and NECs. PATIENTS AND METHODS: Thirteen samples were selected for next-generation sequencing (NGS) analysis with a 425-gene panel. Microsatellite instability (MSI) and tumor mutational burden (TMB) were calculated as well as immunohistochemistry (IHC) was used to test for protein expression. RESULTS: Genetic alterations were very common in NECs, but rare in NETs. The average TMB of NETs and NECs was 2.3 and 6.9, respectively. The TMB of NECs was significantly higher compared to NETs. The TP53 mutation rate was significantly higher in NECs than in NETs (100% vs. 20%), other mutations involved MTOR (n = 2, 15.4%), DDR2 (n = 3, 23.1%), ERBB4 (n = 1, 7.7%), BRCA1 (n = 1, 7.7%), BRCA2 (n = 1, 7.7%), ATM (n = 1, 7.7%), and SMAD4 (n = 1, 7.7%). Deep loss of SMAD4 (1/3, 33.3%), SDHB (1/3, 33.3%), RB1 (1/3, 33.3%), and BRCA2 (1/3, 33.3%), high-level amplification of CRKL (1/3, 33.3%), CCNE1(1/3, 33.3%), and MCL1(1/3, 33.3%) were found in NECs. The integrated analysis found these genetic alterations frequently involve DNA repair and cell cycle, PI3K/AKT/mTOR and TGF-ß/SMAD4 signaling pathways. CONCLUSION: Genetic alterations were very common in NECs and rare in NETs, and frequently involved three main signaling pathways. NEC patients harboring these genetic alterations may benefit from targeted therapy and PD-1/PD-L1 immunotherapy.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus.

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

The correlation between transcription factor 7-like 2 gene polymorphisms and susceptibility of gestational diabetes mellitus in the population of central China: A case-control study.

Objective: To investigate the correlation between transcription factor 7-like 2 (TCF7L2) gene polymorphisms and gestational diabetes mellitus (GDM) risk in the central Chinese population. Methods: This case-control study examined the association of seven TCF7L2 gene single-nucleotide polymorphisms (SNPs) (rs11196218, rs4506565, rs7895340, rs7901695, rs11196205, rs12243326, and rs290487) with GDM risk in the central Chinese population (843 GDM and 877 controls). The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test, Fisher's exact test, and logistic regression were performed. Results: Differences in age, pre-pregnant body mass index (BMI), and family history of type 2 diabetes mellitus (T2DM) between the case and control groups were significant (p < 0.05). Compared with the wild-type genotype, pregnant women with genotypes of rs4506565-AT (OR = 1.89, 95%CI: 1.18-3.02), rs7895340 GA (OR = 1.93, 95%CI: 1.06-3.54), rs7901695-TC (OR = 1.79, 95%CI: 1.11-2.88), and rs11196205-GC (OR = 2.15, 95%CI: 1.16-3.98) had a significantly higher risk of GDM, adjusted by age, pre-pregnant BMI, and family history of T2DM. Functional annotation showed that all these four SNPs fell in the functional elements of human pancreatic islets. Further cumulative effects analysis concluded that when participants carried all these four risk genotypes, the risk of GDM was 3.51 times (OR = 3.51, 95%CI: 1.38-8.90) than that of those without any risk genotypes. Conclusions: The findings of this study suggested that rs4506565, rs7895340, rs7901695, and rs11196205 were the genetic susceptibility SNPs of GDM in the central Chinese population. Further studies are needed to validate our findings and clarify the underlying mechanisms.

Deep reinforcement learning guided graph neural networks for brain network analysis.

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks.

A Review: PI3K/AKT/mTOR Signaling Pathway and Its Regulated Eukaryotic Translation Initiation Factors May Be a Potential Therapeutic Target in Esophageal Squamous Cell Carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a malignant tumor developing from the esophageal squamous epithelium, and is the most common histological subtype of esophageal cancer (EC). EC ranks 10th in morbidity and sixth in mortality worldwide. The morbidity and mortality rates in China are both higher than the world average. Current treatments of ESCC are surgical treatment, radiotherapy, and chemotherapy. Neoadjuvant chemoradiotherapy plus surgical resection is recommended for advanced patients. However, it does not work in the significant promotion of overall survival (OS) after such therapy. Research on targeted therapy in ESCC mainly focus on EGFR and PD-1, but neither of the targeted drugs can significantly improve the 3-year and 5-year survival rates of disease. Phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) pathway is an important survival pathway in tumor cells, associated with its aggressive growth and malignant progression. Specifically, proliferation, apoptosis, autophagy, and so on. Related genetic alterations of this pathway have been investigated in ESCC, such as PI3K, AKT and mTOR-rpS6K. Therefore, the PI3K/AKT/mTOR pathway seems to have the capability to serve as research hotspot in the future. Currently, various inhibitors are being tested in cells, animals, and clinical trials, which targeting at different parts of this pathway. In this work, we reviewed the research progress on the PI3K/AKT/mTOR pathway how to influence biological behaviors in ESCC, and discussed the interaction between signals downstream of this pathway, especially eukaryotic translation initiation factors (eIFs) and the development and progression of ESCC, to provide reference for the identification of new therapeutic targets in ESCC.

Melatonin Receptor 1B Genetic Variants on Susceptibility to Gestational Diabetes Mellitus: A Hospital-Based Case-Control Study in Wuhan, Central China.

Purpose: The aim of the study was to find out the associations of Melatonin receptor 1B (MTNR1B) genetic variants with gestational diabetes mellitus (GDM) in Wuhan of central China. Patients and Methods: A hospital-based case-control study that included 1679 women was carried out to explore the associations of MTNR1B single nucleotide polymorphisms (SNPs) with GDM risk, which were analyzed through logistic regression analysis by adjusting age, pre-pregnancy BMI and family history of diabetes. Multifactor dimensionality reduction was applied to determine gene-gene interactions between SNPs. Results: MTNR1B SNPs rs10830962, rs10830963, rs1387153, rs7936247 and rs4753426 were significantly associated with GDM risk (P<0.05). The rs10830962/G, rs10830963/G, rs1387153/T, and rs7936247/T were risk variants, whereas rs4753426/T was protective variant for GDM development. Fasting plasma glucose (FPG) and 1h-plasma glucose (PG) were significantly different among genotypes at rs10830962 and rs10830963, whereas 2h-PG levels were not. Gene-gene interactions were not found among the five SNPs on GDM risk. Conclusion: MTNR1B genetic variants have significant associations but no gene-gene interactions with GDM risk in central Chinese population. Furthermore, MTNR1B SNPs have significant relationships with glycemic traits.

Association of glucokinase gene and glucokinase regulatory protein gene polymorphisms with gestational diabetes mellitus: A case-control study.

OBJECTIVES: The aim of this study was to investigate the association of glucokinase (GCK) gene, glucokinase regulatory protein (GCKR) gene polymorphisms with the susceptibility to GDM in Chinese population. RESEARCH DESIGN AND METHODS: This case-control study included 835 GDM patients and 870 non-diabetic pregnant women who had their prenatal examinations at 24-28 gestational weeks at the Maternal and Child Health Hospital of Hubei Province from January 15, 2018 to March 31, 2019. The nurses were trained to collect clinical information and blood samples. The candidate single nucleotide polymorphism (SNPs, GCK rs1799884, rs4607517, rs10278336, rs2268574, rs730497 and GCKR rs780094, rs1260326) were genotyped on Sequenom Massarray platform. Statistical analysis including independent sample t test, chi-square test, logistic regression and one-way ANOVA were performed to evaluate the differences in allele and genotype distributions and their correlations with the odds of GDM. RESULTS: There were statistically significant differences in age, pre-gestational BMI, education level and family history of diabetes between case and control group (P < 0.05). After adjusting for these confounders, GCK rs1799884 was still significantly associated with GDM (P < 0.05), but there were no significant associations between rs4607517, rs10278336 and rs2268574, rs780094 and rs1260326 polymorphisms and GDM odds (P > 0.05). In addition, the pregnant women with rs4607517 TT genotype had the significantly higher fasting blood glucose level than CC genotype (P < 0.05). CONCLUSION: GCK rs1799884 mutation is associated with higher GDM odds in Chinese population. Further larger studies are needed to explore the association between GCK and GCKR polymorphisms and GDM susceptibility.

Rational Design of ZIF-8 for Constructing Luminescent Biosensors with Glucose Oxidase and AIE-Type Gold Nanoclusters.

The development of modern technologies has acclimatized biosensors to complicated applicable scenarios with integrated properties as a whole instead of the pursuit of a single-point breakthrough. Here, we targeted a few concerns in the development of enzyme-based biosensors, including stability, analyte enrichment, and signal transduction, and developed a general biosensing model utilizing enzymes, aggregation-induced emission (AIE) luminogens, and stimuli-responsive framework materials as the units. We propose such proof-of-concept of glucose biosensors by coencapsulating glucose oxidase and AIE-type gold nanoclusters into acid-sensitive zeolite imidazolate framework (ZIF)-8 nanocrystals. The acid-activated degradation of ZIF-8 bridges the molecular signals produced by the enzyme-catalytic reaction of glucose and the photon signals generated by ZIF-8-induced AIE effects of gold nanoclusters, resulting in the "turn-off" model nanoprobes for glucose detection with high selectivity. After embedding the nanoprobes into hollow-out tapes, the formed paper biosensors can conveniently detect glucose with the help of a smartphone.

Morusin enhances the antitumor activity of MAPK pathway inhibitors in BRAF-mutant melanoma by inhibiting the feedback activation of STAT3.

BACKGROUND: BRAF and MEK inhibitors significantly prolonged the progression-free survival of patients with BRAF mutant melanoma, but their long-term efficacy was limited by drug resistance. Our previous studies found that targeted inhibition of the mitogen-activated protein kinases (MAPK) pathway promotes the activation of the Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway in BRAF-mutant melanoma cells. Whether dual inhibition of MAPK and JAK2/STAT3 pathways can reverse drug resistance in melanoma remains unclear. METHODS: This study verified whether morusin could enhance the inhibitory effect of MAPK pathway inhibitors on BRAF mutant melanoma by inhibiting the feedback activation of STAT3 at the cellular and animal levels. RESULTS: We demonstrated that morusin could enhance the inhibitory effect of MAPK pathway inhibitors on BRAF mutant melanoma cells by inhibiting the feedback activation of the STAT3/SOX2 pathway. Moreover, our study showed morusin combined with MAPK pathway inhibitors specifically inhibited BRAF-mutant melanoma cells to a greater extent than wild-type cells. Our results also showed that the combination of morusin and BRAF inhibitors could jointly inhibit BRAF mutant melanoma in vivo. Finally, our experiment also revealed that the combination therapy of morusin and MAPK pathway inhibitors jointly inhibited drug-resistant melanoma in vitro and in vivo. CONCLUSION: Our results suggested that the combination of morusin and MAPK pathway inhibitors may be a more effective treatment strategy for BRAF-mutant melanoma than MAPK pathway inhibitors alone.

Encapsulation of Enzymes in Metal-Phenolic Network Capsules for the Trigger of Intracellular Cascade Reactions.

Nanoengineered capsules encapsulated with functional cargos (e.g., enzymes) are of interest for various applications including catalysis, bioreactions, sensing, and drug delivery. Herein, we report a facile strategy to engineer enzyme-encapsulated metal-phenolic network (MPN) capsules using enzyme-loaded zeolitic imidazolate framework nanoparticles (ZIF-8 NPs) as templates, which can be removed in a mild condition (e.g., ethylenediaminetetraacetic acid (EDTA) solution). The capsule size (from 250 nm to 1 µm) and thickness (from 9.8 to 33.7 nm) are well controlled via varying the template size and coating time, respectively. Importantly, MPN capsules encapsulated with enzymes (i.e., glucose oxidase) can trigger the intracellular cascade reaction via the exhaustion of glucose to produce H2O2 and subsequently generate toxic hydroxyl radicals (•OH) based on the Fenton reaction via the reaction between H2O2 and iron ions in MPN coatings. The intracellular cascade reaction for the generation of •OH is efficient to inhibit cancer cell viability, which is promising for the application in chemodynamic therapy.

Association of vitamin D receptor gene polymorphisms with gestational diabetes mellitus-a case control study in Wuhan, China.

BACKGROUND: Gestational diabetes mellitus (GDM) increased risk of perinatal complications for both the women and the fetuses. The association between the vitamin D receptor (VDR) gene polymorphism and GDM has not been thoroughly investigated in Chinese pregnant women. Therefore, we aimed to determine whether VDR gene single nucleotide polymorphisms (SNPs) rs154410, rs7975232, rs731236, rs2228570 and rs739837 contribute to GDM risk in Wuhan, China. Moreover, we aimed to explore their combined effects on the risk of GDM. METHODS: Pregnant women who had prenatal examinations at 24 to 28 weeks' gestation in our hospital from January 15, 2018 to March 31, 2019 were included in this case-control study. After exclusion, a total of 1684 pregnant women (826 GDM patients and 858 non-diabetic controls) were recruited. The clinical information and blood samples were collected by trained interviewers and nurses. Genotyping of candidate SNPs was conducted on the Sequenom MassARRAY platform. Statistical analyses including t-test, ANOVA, chi-square test and logistic regression were performed to the data with SPSS Software to evaluate differences in genotype distribution and associations with GDM risk. Multifactor dimensionality reduction method was used to explore the gene-gene interactions on the risk of GDM. RESULTS: Differences in age, pre-pregnancy BMI, family history of diabetes and previous history of GDM between the case and control groups were statistically significant (P < 0.05), whereas no significant differences were found in height, gravidity, parity, and age of menarche (P > 0.05). There were no significant differences at genotype distributions of the examined VDR gene SNPs (P > 0.05). After adjusting by age, pre-pregnancy BMI, family history of diabetes, the results of logistic regression analysis showed no associations of the five SNPs with GDM in all the four genotype models(P > 0.05). Furthermore, there were no gene-gene interactions on the GDM risk among the five examined VDR gene SNPs. CONCLUSIONS: The VDR gene SNPs rs154410, rs7975232, rs731236, rs2228570 and rs739837 showed neither significant associations nor gene-gene interactions with GDM in Wuhan, China.

New strain of Pediococcus pentosaceus alleviates ethanol-induced liver injury by modulating the gut microbiota and short-chain fatty acid metabolism.

BACKGROUND: Intestinal dysbiosis has been shown to be associated with the pathogenesis of alcoholic liver disease (ALD), which includes changes in the microbiota composition and bacterial overgrowth, but an effective microbe-based therapy is lacking. Pediococcus pentosaceus (P. pentosaceus) CGMCC 7049 is a newly isolated strain of probiotic that has been shown to be resistant to ethanol and bile salts. However, further studies are needed to determine whether P. pentosaceus exerts a protective effect on ALD and to elucidate the potential mechanism. AIM: To evaluate the protective effect of the probiotic P. pentosaceus on ethanol-induced liver injury in mice. METHODS: A new ethanol-resistant strain of P. pentosaceus CGMCC 7049 was isolated from healthy adults in our laboratory. The chronic plus binge model of experimental ALD was established to evaluate the protective effects. Twenty-eight C57BL/6 mice were randomly divided into three groups: The control group received a pair-fed control diet and oral gavage with sterile phosphate buffered saline, the EtOH group received a ten-day Lieber-DeCarli diet containing 5% ethanol and oral gavage with phosphate buffered saline, and the P. pentosaceus group received a 5% ethanol Lieber-DeCarli diet but was treated with P. pentosaceus. One dose of isocaloric maltose dextrin or ethanol was administered by oral gavage on day 11, and the mice were sacrificed nine hours later. Blood and tissue samples (liver and gut) were harvested to evaluate gut barrier function and liver injury-related parameters. Fresh cecal contents were collected, gas chromatography-mass spectrometry was used to measure short-chain fatty acid (SCFA) concentrations, and the microbiota composition was analyzed using 16S rRNA gene sequencing. RESULTS: The P. pentosaceus treatment improved ethanol-induced liver injury, with lower alanine aminotransferase, aspartate transaminase and triglyceride levels and decreased neutrophil infiltration. These changes were accompanied by decreased levels of endotoxin and inflammatory cytokines, including interleukin-5, tumor necrosis factor-α, granulocyte colony-stimulating factor, keratinocyte-derived protein chemokine, macrophage inflammatory protein-1α and monocyte chemoattractant protein-1. Ethanol feeding resulted in intestinal dysbiosis and gut barrier disruption, increased relative abundance of potentially pathogenic Escherichia and Staphylococcus, and the depletion of SCFA-producing bacteria, such as Prevotella, Faecalibacterium, and Clostridium. In contrast, P. pentosaceus administration increased the microbial diversity, restored the relative abundance of Lactobacillus, Pediococcus, Prevotella, Clostridium and Akkermansia and increased propionic acid and butyric acid production by modifying SCFA-producing bacteria. Furthermore, the levels of the tight junction protein ZO-1, mucin proteins (mucin [MUC]-1, MUC-2 and MUC-4) and the antimicrobial peptide Reg3ß were increased after probiotic supplementation. CONCLUSION: Based on these results, the new strain of P. pentosaceus alleviated ethanol-induced liver injury by reversing gut microbiota dysbiosis, regulating intestinal SCFA metabolism, improving intestinal barrier function, and reducing circulating levels of endotoxin and proinflammatory cytokines and chemokines. Thus, this strain is a potential probiotic treatment for ALD.

Vital members in the gut microbiotas altered by two probiotic Bifidobacterium strains against liver damage in rats.

BACKGROUND: Probiotics are effective to rectify the imbalanced gut microbiota in the diseased cohorts. Two Bifidobacterium strains (LI09 and LI10) were found to alleviate D-galactosamine-induced liver damage (LD) in rats in our previous work. A series of bioinformatic and statistical analyses were performed to determine the vital bacteria in the gut microbiotas altered by the LI09 or LI10 in rats. RESULTS: Two groups of representative phylotypes could distinguish the gut microbiotas of LI09 or LI10 groups from the other groups. Among them, OTU170_Porphyromonadaceae acted as a gatekeeper in LI09 group, while OTU12_Bacteroides was determined with multiple correlations in the gut network of LI10 group. Multiple reduced OTUs associated with LC and increased OTUs associated with health were determined in LI09 or LI10 groups, among which, increased OTU51_Barnesiella and reduced OTU99_Barnesiella could be associated with the protective effects of both the two probiotics. The gut microbiotas in LI09, LI10 and positive control groups were clustered into three clusters, i.e., Cluster_1_Microbiota, Cluster_2_Microbiota and Cluster_3_Microbiota, by Partition Around Medoids clustering analysis. Cluster_2_Microbiota was determined at least dysbiotic status due to its greatest LD dysbiosis ratio, lowest levels of liver function variables and plasma cytokines compared with the two other clustered microbiotas, suggesting the treated rats in Cluster_2 were at better health status. CONCLUSION: Our findings suggest that OTU170_Porphyromonadaceae and OTU12_Bacteroides are vital in the gut microbiotas altered by LI09 and LI10. Characteristics of the LD cohorts treated by LI09 or LI10 at different gut microbial colonization states could help monitor the cohorts' health status.

Injectable and Sprayable Polyphenol-Based Hydrogels for Controlling Hemostasis.

Injectable and sprayable hydrogels have attracted considerable attention for application in the biomedical field owing to their high moldability and efficiency in encapsulating therapeutics and cells. Herein, we report the spontaneous assembly of injectable and sprayable hydrogels via a one-step mixing of solutions of tannic acid (TA) and O-carboxymethyl chitosan (CMCS) without an external stimulus. The presence of 1,4-benzenediboronic acid (BDBA) improves the mechanical properties and reduces the gelation time of the resulting hydrogels. The hydrogels assemble via hydrogen bonds between TA and CMCS as well as via dynamic boronate ester bonds between TA and BDBA, as confirmed by Fourier transform infrared spectroscopy. Balancing the interactions between the three components (CMCS/TA/BDBA) is essential for the construction of the hydrogels. The moduli of the CMCS-TA-BDBA hydrogels initially increase as the amount of BDBA increases and decrease after reaching a maximum value at a BDBA-to-TA molar ratio of 3:1. The CMCS-TA-BDBA hydrogels with interconnected porous morphologies display rapid gelation (∼10 s), biocompatibility, self-healing, injectable, and sprayable abilities. In addition, the hydrogels can be used for hemostasis. The extent of bleeding in mouse livers treated with the hydrogels could be reduced extensively from 240 (nontreated mouse livers) to 55 mg (77% reduction). The reported hydrogels coupled with the combination of functionality and biological activity make them promising hemostatic materials for biomedical applications.