Spiral time-of-flight magnetic resonance angiography for intracranial vascular imaging: performance compared to conventional Cartesian angiogram.

Background: Computed tomography angiography (CTA) and digital subtraction angiography (DSA) usually raise the risk of potential malignancies with cumulative radiation doses. Current time-of-flight magnetic resonance angiography (TOF-MRA) (dubbed as cTOF), which is based on Cartesian sampling mode, may show limited diagnostic conspicuity at sinuous or branching regions. It is also prone to relatively high false positive diagnoses and undesirable display of distal intracranial vessels. This study aimed to use spiral TOF-MRA (sTOF) as a noninvasive alternative to explore possible improvement, such that the application of magnetic resonance angiography (MRA) can be extended to facilitate clinical examination or cerebrovascular disease diagnosis and follow-up studies. Methods: Initially, 37 patients with symptoms of dizziness or transient ischemic attack were consecutively recruited for suspected intracranial vascular disease examination from Zhongshan Hospital of Xiamen University between July 2020 and April 2021 in this cross-sectional prospective study. After excluding 1 patient with severe scanning artifacts, 1 patient whose scanning scope did not meet the requirement, and 1 patient with confounding tumor lesions, a total of 34 participants were included according to the inclusion and exclusion criteria. Each participant underwent intracranial vascular imaging with both sTOF and cTOF sequences on a 3.0 T MR scanner with a conventional head-neck coil of 16 channels. Contrast CTA or DSA was also performed for 15 patients showing pathology. Qualitative comparisons in terms of image quality and diagnostic efficacy ratings, quantitative comparisons in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel length, and sharpness were evaluated. Pair-wise Wilcoxon test was performed to evaluate the imaging quality derived from cTOF and sTOF acquisitions and weighted Cohen's Kappa was conducted to assess the rating consistency between different physicians. Results: Compared to cTOF, sTOF showed better performance with fewer artifacts. It can effectively alleviate false positives of normal vessels being misdiagnosed as aneurysm or stenosis. Improved conspicuity was observed in cerebral distal regions with more clearly identifiable vasculature at finer scales. Quantitative comparisons in selected regions revealed significant improvement of sTOF in SNR (P<0.01 or P<0.001), CNR (P<0.001), vessel length (P<0.001), and sharpness (P<0.001) as compared to cTOF. Besides, sTOF can depict details of M1 and M2 segments of middle cerebral artery (MCA) at metallic implant region, showing its resistance to magnetic susceptibility. Conclusions: The sTOF shows higher imaging quality and lesion detectability with reduced artifacts and false positives, representing a potentially feasible surrogate in intracranial vascular imaging for future clinic routines.

Gut microbiota plays a significant role in gout.

With the development of social economy, the incidence of gout is increasing, which is closely related to people's increasingly rich diet. Eating a diet high in purine, fat, sugar and low-fibre for a long time further aggravates gout by affecting uric acid metabolism. The renal metabolism mechanism of uric acid has been thoroughly studied. To find a new treatment method for gout, increasing studies have recently been conducted on the mechanism of intestinal excretion, metabolism and absorption of uric acid. The most important research is the relationship between intestinal microbiota and the risk of gout. Gut microbiota represent bacteria that reside in a host's gastrointestinal tract. The composition of the gut microbiota is associated with protection against pathogen colonization and disease occurrence. This review focuses on how gut microbiota affects gout through uric acid and discusses the types of bacteria that may be involved in the occurrence and progression of gout. We also describe potential therapy for gout by restoring gut microbiota homeostasis and reducing uric acid levels. We hold the perspective that changing intestinal microbiota may become a vital method for effectively preventing or treating gout.

The impact of inflammatory biomarkers on amputation rates in patients with diabetic foot ulcers.

Diabetic Foot Ulcers (DFUs) are a major complication of diabetes, often leading to amputation. Understanding the relationship between haematological inflammatory markers and the incidence of amputation in DFU patients with infectious complications is crucial for improving management and outcomes. This retrospective study, conducted from May 2020 to October 2022, involved 109 patients with DFUs, categorised into amputation (AM) and non-amputation (NAM) groups. Patients were evaluated for various factors, including demographic data, DFU duration, and blood parameters such as haemoglobin A1c (HbA1c), haemoglobin (Hb), albumin (ALB), white blood cell count (WBC), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR). Statistical analyses were performed using independent sample t-tests, Mann-Whitney U test and logistic regression. The univariate analysis showed no significant difference in BMI, DM duration or DFU duration between groups. However, significant differences were noted in PCT, Hb, ESR, ALB, HbA1c and WBC levels, and in inflammatory ratios (NLR, PLR and LMR). Multivariate logistic regression identified CRP, NLR and PLR as independent risk factors for amputation. The study highlights CRP, PLR and NLR as key independent risk factors for amputation in patients with DFUs. These easily obtainable markers from routine blood tests can effectively aid in predicting the risk of osteomyelitis and amputation, enhancing clinical decision making and patient care strategies.

Preventive Effects of Melatonin on Delirium in Intensive Care Unit Patients: A Meta-Analysis of Randomized Controlled Trials.

Aims: To investigate the preventive efficacy of melatonin on the incidence of delirium and other clinical outcomes of subjects in the intensive care unit (ICU). Methods: Randomized controlled trials concerning the effects of melatonin on delirium published from inception to July 2022 were identified from PubMed, Embase, and the Cochrane Library. The primary outcome was delirium incidence. The secondary outcome was the length of ICU stay, the duration of mechanical ventilation, and the mortality in ICU. A meta-analysis was performed. Estimates were presented as risk ratio (RR) or standard mean difference (SMD) with 95% confidence interval (CI). Results: Eleven RCTs with 2002 patients were included. The forest plots showed that the delirium incidence did not significantly decrease after melatonin administration (RR 0.85; 95% CI, 0.61~1.18, P = .32, I2=60%, P for heterogeneity = .01). The subgroup analyses confirmed that melatonin significantly reduced the incidence of delirium (RR 0.70; 95% CI, 0.56~0.89, P = .003, I2 = 32%, P for heterogeneity = .22) for the special ICU patients. Also, for ICU patients, the length of ICU stays, duration of mechanical ventilation, and mortality were not significantly decreased after melatonin treatment (all P > .05). Conclusion: Melatonin may decrease the incidence of delirium for special ICU patients. PROSPERO registration number: CRD42022354874.

Construction of optical dual-mode sensing platform based on green emissive carbon quantum dots for effective detection of ClO- and cellular imaging.

Hypochlorite (ClO-) is an important redox regulator in reactive oxygen species, which play a considerable role in oxidative stress and related diseases. Hence, accurate and sensitive monitoring of ClO- concentration was urgently needed in the fields of life sciences, food and environment. Bright green fluorescent carbon quantum dots (G-CQDs) were synthesized utilizing one-step hydrothermal method with citric acid and acriflavine precursors. Through TEM, FTIR, XPS and zeta potential characterization procedures, G-CQDs illustrated uniformly dispersed and significant number of -NH2 and -OH on the surface. Meanwhile, the fluorescence and colorimetric analysis exhibited wide linear range and low detection limit response to ClO-. The fluorescence changes of G-CQDs were identified via smartphone to realize mobile sensing of ClO-. Subsequently, G-CQDs was applied for visualization and quantitative detection of ClO- in drinking water samples with satisfactory recovery rate. More importantly, G-CQDs demonstrated good water solubility, optical stability and excellent biocompatibility, which offered a promising analysis approach in cell imaging and exogenous ClO- detection in living cells. G-CQDs illustrated bright prospect and great potential in practical application of ClO- associated disease prevention and early clinical diagnosis.

Recent Advances on the Molecular Mechanism and Clinical Trials of Venous Thromboembolism.

Venous thromboembolism is a condition that includes deep vein thrombosis and pulmonary embolism. It is the third most common cardiovascular disease behind acute coronary heart disease and stroke. Over the past few years, growing research suggests that venous thrombosis is also related to the immune system and inflammatory factors have been confirmed to be involved in venous thrombosis. The role of inflammation and inflammation-related biomarkers in cerebrovascular thrombotic disease is the subject of ongoing debate. P-selectin leads to platelet-monocyte aggregation and stimulates vascular inflammation and thrombosis. The dysregulation of miRNAs has also been reported in venous thrombosis, suggesting the involvement of miRNAs in the progression of venous thrombosis. Plasminogen activator inhibitor-1 (PAI-1) is a crucial component of the plasminogen-plasmin system, and elevated levels of PAI-1 in conjunction with advanced age are significant risk factors for thrombosis. In addition, it has been showed that one of the ways that neutrophils promote venous thrombosis is the formation of neutrophil extracellular traps (NETs). In recent years, the role of extracellular vesicles (EVs) in the occurrence and development of VTE has been continuously revealed. With the advancement of research technology, the complex regulatory role of EVs on the coagulation process has been gradually discovered. However, our understanding of the causes and consequences of these changes in venous thrombosis is still limited. Therefore, we review our current understanding the molecular mechanisms of venous thrombosis and the related clinical trials, which is crucial for the future treatment of venous thrombosis.

MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder.

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

EDIL3 and VEGF Synergistically Affect Angiogenesis in Endothelial Cells.

Background: Angiogenesis is one of the histologically predominant characteristics of psoriasis. Vascular endothelial growth factor (VEGF) and epidermal growth factor-like repeats and discoidin I-like domains 3 (EDIL3) have critical effects on angiogenesis. Both these proteins are vital proangiogenic factors in tumor occurrence and progression; however, the relationship between EDIL3 and VEGF with psoriasis remains unclear. Objective: We aimed to elucidate the role of EDIL3 and VEGF and the involved mechanisms in psoriasis-associated angiogenesis. Methods: EDIL3 and VEGF expression in cutaneous tissue was determined by immunohistochemical assay. The effects of EDIL3 on VEGF, VEGFR2, and the growth, migration, and tube formation of human umbilical vein endothelial cells (HUVECs) were analyzed by Western blotting assay, cell counting kit-8 assay, Transwell assay, and Matrigel tube formation assay. Results: EDIL3 and VEGF levels in psoriatic lesions significantly increased as compared to those in normal individuals and showed a positive relationship with the Psoriasis Area and Severity Index. The downregulation of EDIL3 decreased VEGF and VEGFR2 expression in HUVECs. Moreover, the decreased expression of EDIL3 and VEGF reduced the growth, invasion, and tube formation abilities of HUVECs, while EDIL3 resistance to VEGF and VEGFR2 was restored by using the EDIL3 recombinant protein. Conclusion: These results suggest that psoriasis is also characterized by EDIL3 and VEGF-mediated angiogenesis. Thus, EDIL3 and VEGF could serve as novel targets for treating psoriasis.

Dual Multiscale Mean Teacher Network for Semi-Supervised Infection Segmentation in Chest CT Volume for COVID-19.

Automated detecting lung infections from computed tomography (CT) data plays an important role for combating coronavirus 2019 (COVID-19). However, there are still some challenges for developing AI system: 1) most current COVID-19 infection segmentation methods mainly relied on 2-D CT images, which lack 3-D sequential constraint; 2) existing 3-D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3-D volume; and 3) the emergent breaking out of COVID-19 makes it hard to annotate sufficient CT volumes for training deep model. To address these issues, we first build a multiple dimensional-attention convolutional neural network (MDA-CNN) to aggregate multiscale information along different dimension of input feature maps and impose supervision on multiple predictions from different convolutional neural networks (CNNs) layers. Second, we assign this MDA-CNN as a basic network into a novel dual multiscale mean teacher network (DM [Formula: see text]-Net) for semi-supervised COVID-19 lung infection segmentation on CT volumes by leveraging unlabeled data and exploring the multiscale information. Our DM [Formula: see text]-Net encourages multiple predictions at different CNN layers from the student and teacher networks to be consistent for computing a multiscale consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from multiple predictions of MDA-CNN. Third, we collect two COVID-19 segmentation datasets to evaluate our method. The experimental results show that our network consistently outperforms the compared state-of-the-art methods.

Spatially Confined PdHx Metallenes by Tensile Strained Atomic Ru Layers for Efficient Hydrogen Evolution.

Hydride metallenes show great potential for hydrogen-related catalytic applications due to favorable electronic structures modulated by interstitial hydrogen atoms and large active surface areas of metallenes. Metallene nanostructures generally have compressive strain relative to bulk, which can affect both the stability and the catalytic behavior of hydride metallenes but in general cannot be controlled. Here, we demonstrate highly stable PdHx metallenes with a tensile strained Ru surface layer and reveal the spatial confinement effect of the Ru skin by multiple spectroscopic characterizations and molecular dynamics simulations. These PdHx@Ru metallenes with a 4.5% expanded Ru outer layer exhibit outstanding alkaline hydrogen evolution reaction activity with a low overpotential of 30 mV at 10 mA cm-2 and robust stability with negligible activity decay after 10,000 cycles, which are superior to commercial Pt/C and most reported Ru-based electrocatalysts. Control experiments and first-principles calculations reveal that the tensile strained Ru outer layer lowers the energy barrier of H2O dissociation and provides a moderate hydrogen adsorption energy.

Resveratrol in the Hypothalamic Paraventricular Nucleus Attenuates Hypertension by Regulation of ROS and Neurotransmitters.

BACKGROUND: The hypothalamic paraventricular nucleus (PVN) is an important nucleus in the brain that plays a key role in regulating sympathetic nerve activity (SNA) and blood pressure. Silent mating-type information regulation 2 homolog-1 (sirtuin1, SIRT1) not only protects cardiovascular function but also reduces inflammation and oxidative stress in the periphery. However, its role in the central regulation of hypertension remains unknown. It is hypothesized that SIRT1 activation by resveratrol may reduce SNA and lower blood pressure through the regulation of intracellular reactive oxygen species (ROS) and neurotransmitters in the PVN. METHODS: The two-kidney one-clip (2K1C) method was used to induce renovascular hypertension in male Sprague-Dawley rats. Then, bilaterally injections of vehicle (artificial cerebrospinal fluid, aCSF, 0.4 µL) or resveratrol (a SIRT1 agonist, 160 µmol/L, 0.4 µL) into rat PVN were performed for four weeks. RESULTS: PVN SIRT1 expression was lower in the hypertension group than the sham surgery (SHAM) group. Activated SIRT1 within the PVN lowered systolic blood pressure and plasma norepinephrine (NE) levels. It was found that PVN of 2K1C animals injected with resveratrol exhibited increased expression of SIRT1, copper-zinc superoxide dismutase (SOD1), and glutamic acid decarboxylase (GAD67), as well as decreased activity of nuclear factor-kappa B (NF-κB) p65 and NAD(P)H oxidase (NOX), particularly NOX4. Treatment with resveratrol also decreased expression of ROS and tyrosine hydroxylase (TH). CONCLUSION: Resveratrol within the PVN attenuates hypertension via the SIRT1/NF-κB pathway to decrease ROS and restore the balance of excitatory and inhibitory neurotransmitters.

PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.

Peptide detectability is defined as the probability of identifying a peptide from a mixture of standard samples, which is a key step in protein identification and analysis. Exploring effective methods for predicting peptide detectability is helpful for disease treatment and clinical research. However, most existing computational methods for predicting peptide detectability rely on a single information. With the increasing complexity of feature representation, it is necessary to explore the influence of multivariate information on peptide detectability. Thus, we propose an ensemble deep learning method, PD-BertEDL. Bidirectional encoder representations from transformers (BERT) is introduced to capture the context information of peptides. Context information, sequence information, and physicochemical information of peptides were combined to construct the multivariate feature space of peptides. We use different deep learning methods to capture the high-quality features of different categories of peptides information and use the average fusion strategy to integrate three model prediction results to solve the heterogeneity problem and to enhance the robustness and adaptability of the model. The experimental results show that PD-BertEDL is superior to the existing prediction methods, which can effectively predict peptide detectability and provide strong support for protein identification and quantitative analysis, as well as disease treatment.

N-acetylcysteine regulates dental follicle stem cell osteogenesis and alveolar bone repair via ROS scavenging.

BACKGROUND: Dental follicle stem cells (DFSCs) show mesenchymal stem cell properties with the potential for alveolar bone regeneration. Stem cell properties can be impaired by reactive oxygen species (ROS), prompting us to examine the importance of scavenging ROS for stem cell-based tissue regeneration. This study aimed to investigate the effect and mechanism of N-acetylcysteine (NAC), a promising antioxidant, on the properties of DFSCs and DFSC-based alveolar bone regeneration. METHODS: DFSCs were cultured in media supplemented with different concentrations of NAC (0-10 mM). Cytologic experiments, RNA-sequencing and antioxidant assays were performed in vitro in human DFSCs (hDFSCs). Rat maxillary first molar extraction models were constructed, histological and radiological examinations were performed at day 7 post-surgery to investigate alveolar bone regeneration in tooth extraction sockets after local transplantation of NAC, rat DFSCs (rDFSCs) or NAC-treated rDFSCs. RESULTS: 5 mM NAC-treated hDFSCs exhibited better proliferation, less senescent rate, higher stem cell-specific marker and immune-related factor expression with the strongest osteogenic differentiation; other concentrations were also beneficial for maintaining stem cell properties. RNA-sequencing identified 803 differentially expressed genes between hDFSCs with and without 5 mM NAC. "Developmental process (GO:0032502)" was prominent, bioinformatic analysis of 394 involved genes revealed functional and pathway enrichment of ossification and PI3K/AKT pathway, respectively. Furthermore, after NAC treatment, the reduction of ROS levels (ROS, superoxide, hydrogen peroxide), the induction of antioxidant levels (glutathione, catalase, superoxide dismutase), the upregulation of PI3K/AKT signaling (PI3K-p110, PI3K-p85, AKT, phosphorylated-PI3K-p85, phosphorylated-AKT) and the rebound of ROS level upon PI3K/AKT inhibition were showed. Local transplantation of NAC, rDFSCs or NAC-treated rDFSCs was safe and promoted oral socket bone formation after tooth extraction, with application of NAC-treated rDFSCs possessing the best effect. CONCLUSIONS: The proper concentration of NAC enhances DFSC properties, especially osteogenesis, via PI3K/AKT/ROS signaling, and offers clinical potential for stem cell-based alveolar bone regeneration.

The development of a nomogram model for predicting left recurrent laryngeal nerve lymph node metastasis in esophageal cancer based on radiomics and clinical factors.

Background: The lymph node dissection for esophageal cancer is controversial. Some prediction models of lymph node metastasis (LNM) use the short diameter of lymph nodes measured by computed tomography (CT) examination as a predictor, but the size of that for judging metastasis is still controversial. However, radiomics can extract some features in tumors that cannot be obtained by naked eyes, which may have a higher value in predicting LNM. In this study, a nomogram was developed based on radiomics and clinical factors to predict left recurrent laryngeal nerve lymph node (RLNN) metastasis in patients with esophageal squamous cell carcinoma (ESCC). Methods: There were 350 patients included in this retrospective study. And the postoperative pathological results determined whether there was left RLNN metastasis. A univariate analysis was conducted of the clinical data. The least absolute shrinkage and selection operator regression analysis was conducted to filter the radiomics features extracted from CT images. The multivariate logistic regression equation was used to construct a nomogram. The area under the curve (AUC) was used to evaluate the predictive ability. Due to the small sample size, we chose to perform internal validation after the model was established by 10-fold cross-validation, Harrell's concordance index (C-index), bootstrap validation and calibration. Results: Ultimately, 3 indicators were screened out; that is, tumor location, surface volume ratio, and run-length non-uniformity. We then constructed the nomogram using these 3 indicators. The model had good accuracy and calibration performance. It has an AUC of 0.903 (95% confidence interval: 0.861-0.945), a sensitivity of 0.873, and a specificity of 0.756. Ten-fold cross-validation showed that the sensitivity and specificity of the training set were 88.08% and 75.81%, and the validation set had a sensitivity of 85.08% and a specificity of 75.49%. The Brier score was 0.074, and C-index was 0.904, which indicated good consistency between the actual and predicted results. Conclusions: A nomogram constructed based on radiomics features and clinical factors can be used to predict the metastasis of left RLNN in patients with ESCC in a non-invasive way, which provided a reference for clinicians to formulate individualized lymph node dissection plans.

A model for predicting drug-disease associations based on dense convolutional attention network.

The development of new drugs is a time-consuming and labor-intensive process. Therefore, researchers use computational methods to explore other therapeutic effects of existing drugs, and drug-disease association prediction is an important branch of it. The existing drug-disease association prediction method ignored the prior knowledge contained in the drug-disease association data, which provided a strong basis for the research. Moreover, the previous methods only paid attention to the high-level features in the network when extracting features, and directly fused or connected them in series, resulting in the loss of information. Therefore, we propose a novel deep learning model for drug-disease association prediction, called DCNN. The model introduces the Gaussian interaction profile kernel similarity for drugs and diseases, and combines them with the structural similarity of drugs and the semantic similarity of diseases to construct the feature space jointly. Then dense convolutional neural network (DenseCNN) is used to capture the feature information of drugs and diseases, and introduces a convolutional block attention module (CBAM) to weight features from the channel and space levels to achieve adaptive optimization of features. The ten-fold cross-validation results of the model DCNN and the experimental results of the case study show that it is superior to the existing drug-disease association predictors and effectively predicts the drug-disease associations.

Development and validation of a nomogram for predicting severity in patients with hemorrhagic fever with renal syndrome: A retrospective study.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem. METHODS: In this retrospective study, we included a total of 155 consecutive patients who were diagnosed with HFRS, of whom 109 patients served as a training cohort and 46 patients as an independent verification cohort. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. RESULTS: The area under the receiver operating characteristic curve (AUC) of the nomogram indicated that the model had good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. CONCLUSION: In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS would be better identified and patients can be treated early.

Gut microbiota homeostasis restoration may become a novel therapy for breast cancer.

Breast cancer is the most diagnosed cancer in women. It significantly impairs a patient's physical and mental health. Gut microbiota comprise the bacteria residing in a host's gastrointestinal tract. Through studies over the last decade, we now know that alterations in the composition of the gut microbiome are associated with protection against colonization by pathogens and other diseases, such as diabetes and cancer. This review focuses on how gut microbiota can affect breast cancer development through estrogen activity and discusses the types of bacteria that may be involved in the onset and the progression of breast cancer. We also describe potential therapies to curtail the risk of breast cancer by restoring gut microbiota homeostasis and reducing systemic estrogen levels. This review will further explore the relationship between intestinal microbes and breast cancer and propose a method to treat breast cancer by improving intestinal microbes. We aimed at discovering new methods to prevent or treat BC by changing intestinal microorganisms.

Effects of Irbesartan and Amlodipine Besylate Tablets on the Intestinal Microflora of Rats With Hypertensive Renal Damage.

Objective: The present study aimed to investigate the effects of irbesartan and amlodipine besylate tablets on the intestinal microflora of rats with hypertensive renal damage. Methods: Eighteen 12-week-old male spontaneous hypertensive rats were randomly divided into three groups. The Ai-HDG group was given irbesartan at 15 mg/kg per day by gavage, the Ci-HDG group was given amlodipine besylate tablets at 1 mg/kg per day by gavage, and the Wi-HDG group, i.e., the control, was given the same dose of distilled water per day by gavage. The treatment lasted for 6 weeks. Six 12-week-old male Wistar-Kyoto rats were used as the reference group. Bacterial DNA was extracted from the feces of all the rats for high-throughput sequencing before and after the experiment. Operational taxonomic units were used to analyze the species of the intestinal flora, and the alpha diversity index was used to analyze the diversity. The relative abundance of the intestinal microflora in each group of rats was therefore analyzed at the phylum and genus levels. Results: Compared with the Wi-HDG group, the alpha diversity of the Ai-HDG group increased (p < 0.05), while in the Ci-HDG group, only the Shannon index increased significantly. At the phylum level, compared with the control group, in the Ai-HDG and Ci-HDG groups, Firmicutes (F) decreased, Bacteroides (B) increased, and the F/B ratio decreased (p < 0.05). At the genus level, compared with the Wi-HDG group, the Ai-HDG and Ci-HDG groups did not show a significantly delayed decline in lactic acid bacteria. However, in the Ai-HDG group, the relative abundance of Bifidobacteria increased. Conclusion: After the administration of irbesartan and amlodipine besylate, the disorder of intestinal flora in the rats with hypertensive renal damage improved. However, irbesartan was better than amlodipine besylate at improving the diversity of the intestinal flora in these rats.

Hierarchical Micro-Nano Topography Promotes Cell Adhesion and Osteogenic Differentiation via Integrin α2-PI3K-AKT Signaling Axis.

Surface topography dictates important aspects of cell biological behaviors. In our study, hierarchical micro-nano topography (SLM-AHT) with micro-scale grooves and nano-scale pores was fabricated and compared with smooth topography (S) and irregular micro-scale topography (SLA) surfaces to investigate mechanism involved in cell-surface interactions. Integrin α2 had a higher expression level on SLM-AHT surface compared with S and SLA surfaces, and the expression levels of osteogenic markers icluding Runx2, Col1a1, and Ocn were concomitantly upregulated on SLM-AHT surface. Moreover, formation of mature focal adhesions were significantly enhanced in SLM-AHT group. Noticablely, silencing integrin α2 could wipe out the difference of osteogenic gene expression among surfaces with different topography, indicating a crucial role of integrin α2 in topography induced osteogenic differentiation. In addition, PI3K-AKT signaling was proved to be regulated by integrin α2 and consequently participate in this process. Taken together, our findings illustrated that integrin α2-PI3K-AKT signaling axis plays a key role in hierarchical micro-nano topography promoting cell adhesion and osteogenic differentiation.