Platelet Membrane Coated Cu9S8-SNAP for Targeting NIR-II Mild Photothermal Enhanced Chemodynamic/Gas Therapy of Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive and uncommon subtype of breast cancer with a poor prognosis. It is crucial to prioritise the creation of a nanotherapeutic method that is highly selective and actively targeting TNBC. This study explores a new nanosystem, Cu9S8-SNAP@PM (C-S@P), composed of Cu9S8-SNAP coated with a platelet membrane (PM). The purpose of this nanosystem is to cure TNBC using multimodal therapy. The utilisation of PM-coated nanoparticles (NPs) enables active targeting, leading to the efficient accumulation of C-S@P within the tumour. The Cu9S8 component within these NPs serves the potential to exert photothermal therapy (PTT) and chemodynamic therapy (CDT). Simultaneously, the S-Nitroso-N-Acetylvanicillamine (SNAP) component enables nitric oxide (NO) gas therapy (GT). Furthermore, when exposed to NIR-II laser light, Cu9S8 not only increases the temperature of the tumour area for PTT, but also boosts CDT and stimulates the release of NO through thermal reactions to improve the effectiveness of GT. Both in vitro and in vivo experimental results validate that C-S@P exhibits minimal side effects and represents a multifunctional nano-drug targeted at tumors for efficient treatment. This approach promises significant potential for TNBC therapy and broader applications in oncology.

Concentrations, distribution, and key influencing factors of antibiotic resistance genes and bacterial community in water and reared fish tissues in a typical tilapia farm in South China.

Although previous studies have investigated the occurrence of antibiotic resistance genes (ARGs) in aquaculture, few have monitored the concentrations and propagation of ARGs in biological tissues or investigated the key factors influencing their spread in aquaculture. This study investigated the concentration, propagation, and distribution of ARGs and bacterial communities in water sources, pond water, and tilapia tissues, and their key influencing factors, in a typical tilapia farm. ErmF, sul1, and sul2 were the dominant ARGs with high concentrations. The total concentrations of ARGs (TCAs) in tilapia tissues decreased in the following order: stomach > scales > intestine > gills (P < 0.05). Redundancy analysis and multiple linear regression revealed that suspended solids (SS) and chemical oxygen demand (COD) were positively correlated with the dominant ARGs ermF sul2, and the TCAs (P < 0.05); additionally, Chloroflexi and Bacteroidetes in tilapia aquaculture water were positively correlated with the dominant ARGs ermF and sul2, as well as the TCAs (P < 0.05). This study suggests that SS and COD were the key factors driving the distribution and spread of ARGs in tilapia aquaculture water. Additionally, Chloroflexi and Bacteroidetes were the key bacterial flora affecting the propagation of ARGs in tilapia aquaculture systems.

Upregulation of SLC12A3 and SLC12A9 Mediated by the HCP5/miR-140-5p Axis Confers Aggressiveness and Unfavorable Prognosis in Uveal Melanoma.

Perturbation of solute carriers (SLCs) has been implicated in metabolic disorders and cancer, highlighting the potential for drug discovery and therapeutic opportunities. However, there is relatively little exploration of the clinical relevance and potential molecular mechanisms underlying the role of the SLC12 family in uveal melanoma (UVM). Here, we performed an integrative multiomics analysis of the SLC12 family in multicenter UVM datasets and found that high expression of SLC12A3 and SLC12A9 was associated with unfavorable prognosis. Moreover, SLC12A3 and SLC12A9 were highly expressed in UVM in vivo. We experimentally characterized the roles of these proteins in tumorigenesis in vitro and explored their association with the prognosis of UVM. Lastly, we identified the HCP5-miR-140-5p axis as a potential noncoding RNA pathway upstream of SLC12A3 and SLC12A9, which was associated with immunomodulation and may represent a novel predictor for clinical prognosis and responsiveness to checkpoint blockade immunotherapy. These findings may facilitate a better understanding of the SLCome and guide future rationalized development of SLC-targeted therapy and drug discovery for UVM.

Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.

BACKGROUND: Patient-reported outcomes (PROs) can be obtained outside hospitals and are of great significance for evaluation of patients with chronic heart failure (CHF). The aim of this study was to establish a prediction model using PROs for out-of-hospital patients. METHODS: CHF-PRO were collected in 941 patients with CHF from a prospective cohort. Primary endpoints were all-cause mortality, HF hospitalization, and major adverse cardiovascular events (MACEs). To establish prognosis models during the two years follow-up, six machine learning methods were used, including logistic regression, random forest classifier, extreme gradient boosting (XGBoost), light gradient boosting machine, naive bayes, and multilayer perceptron. Models were established in four steps, namely, using general information as predictors, using four domains of CHF-PRO, using both of them and adjusting the parameters. The discrimination and calibration were then estimated. Further analyze were performed for the best model. The top prediction variables were further assessed. The Shapley additive explanations (SHAP) method was used to explain black boxes of the models. Moreover, a self-made web-based risk calculator was established to facilitate the clinical application. RESULTS: CHF-PRO showed strong prediction value and improved the performance of the models. Among the approaches, XGBoost of the parameter adjustment model had the highest prediction performance with an area under the curve of 0.754 (95% CI: 0.737 to 0.761) for death, 0.718 (95% CI: 0.717 to 0.721) for HF rehospitalization and 0.670 (95% CI: 0.595 to 0.710) for MACEs. The four domains of CHF-PRO, especially the physical domain, showed the most significant impact on the prediction of outcomes. CONCLUSION: CHF-PRO showed strong prediction value in the models. The XGBoost models using variables based on CHF-PRO and the patient's general information provide prognostic assessment for patients with CHF. The self-made web-based risk calculator can be conveniently used to predict the prognosis for patients after discharge. CLINICAL TRIAL REGISTRATION: URL: http://www.chictr.org.cn/index.aspx ; Unique identifier: ChiCTR2100043337.

Temporal variations, distribution, and dissemination of antibiotic resistance genes and changes of bacterial communities in a biofloc-based zero-water-exchange mariculture system.

Antibiotic resistance genes (ARGs) have obtained an increasing number of global concerns for the severe risks they pose to food safety and public health. Studies have investigated the concentrations and distribution of ARGs in the environment. However, the distribution and dissemination of ARGs, the bacterial communities, and the key influencing factors during the entire rearing period in the biofloc-based zero-water-exchange mariculture system (BBZWEMS) remain unclear. The current study investigated the concentrations, temporal variations, distribution, and dissemination of ARGs, the changes in the bacterial communities, as well as the key influencing factors during the rearing period in the BBZWEMS. Sul1 and sul2 were dominant ARGs. Total concentrations of ARGs followed a trend of decrease in pond water, while they followed a trend of increase in source water, biofloc, and shrimp gut. Total concentrations of targeted ARGs in the water source were higher than those in the pond water and biofloc samples for each corresponding rearing stage by 2.25-122.97-fold (p < 0.05). The bacterial communities in biofloc and pond water did not change much, while they changed considerably in the shrimp gut samples during the rearing period. Pearson correlation, redundancy analysis, and multivariable linear regression analysis showed that suspended substances and Planctomycetes were positively correlated with the concentrations of ARGs (p < 0.05). The current study indicates that the water source may be a critical source of ARGs, and that suspended substances is a key factor influencing the distribution and dissemination of ARGs in the BBZWEMS. Early intervention measures on ARGs in water sources should be implemented to aid in the prevention and control of resistance genes in aquaculture industry, and reduce the potential risks of ARGs to public health and food safety.

An Efficient Recognition Method for Orbital Angular Momentum via Adaptive Deep ELM.

For orbital angular momentum (OAM) recognition in atmosphere turbulence, how to design a self-adapted model is a challenging problem. To address this issue, an efficient deep learning framework that uses a derived extreme learning machine (ELM) has been put forward. Different from typical neural network methods, the provided analytical machine learning model can match the different OAM modes automatically. In the model selection phase, a multilayer ELM is adopted to quantify the laser spot characteristics. In the parameter optimization phase, a fast iterative shrinkage-thresholding algorithm makes the model present the analytic expression. After the feature extraction of the received intensity distributions, the proposed method develops a relationship between laser spot and OAM mode, thus building the steady neural network architecture for the new received vortex beam. The whole recognition process avoids the trial and error caused by user intervention, which makes the model suitable for a time-varying atmospheric environment. Numerical simulations are conducted on different experimental datasets. The results demonstrate that the proposed method has a better capacity for OAM recognition.

Metagenomic analysis of the abundances, diversity, and distribution of antibiotic resistance genes and their potential bacterial hosts in two types of shrimp-rearing farms in South China.

Antibiotic resistance genes (ARGs) are attracting increasing concern worldwide. Many previous studies have investigated the occurrence and concentrations of ARGs in aquaculture. However, the sources of ARGs and the links with their potential bacterial hosts have not yet been explored. This study investigated the abundances and diversity of ARGs in two types of shrimp farms in South China through metagenomic sequencing. In total, 14 ARG types were detected. Tetracycline was the dominant ARG type. The abundances of ARGs in samples decreased in the order of duck feces > water source > sediment > shrimp gut > pond water. The samples from the duck-shrimp integrated farm contained 1.29-3.81-fold more abundant ARGs than those from the shrimp monoculture farm (p < 0.05). Several ARGs, that were most predominant in the duck feces samples, were also the most predominant in the shrimp gut samples from the duck-shrimp integrated farm. Redundancy analysis indicated that the abundances and distribution of ARGs formed three clusters: duck feces, water samples, and sediment and shrimp gut samples. The dominant genera in duck feces known as human pathogenic bacteria were potential hosts of ARGs, and were also dominant in the shrimp gut samples in the duck-shrimp integrated farm. Additionally, the abundances of dominant genera in the shrimp gut samples of the duck-shrimp integrated farm were 1.74-35.07-fold higher than those in the shrimp monoculture farm (p < 0.01). The duck-shrimp integrated farm had 3.36-fold and 4.94-fold higher abundances of ARGs and mobile genetic elements in the shrimp gut samples than those from the shrimp monoculture farm, respectively (p < 0.05). The results indicate that duck feces may be a crucial source of diverse and abundant ARGs spreading to reared shrimps in duck-shrimp integrated farms, posing a severe risk to public health.

Toxicity of the microcystin-producing cyanobacteria Microcystis aeruginosa to shrimp Litopenaeus vannamei.

Microcystis aeruginosa is reported to cause cyanobacterial blooms in shrimp breeding ponds, which can result in significant shrimp mortality. However, the toxic effects of M. aeruginosa on Litopenaeus vannamei are still not completely understood. In this paper, the toxicity of M. aeruginosa cells to L. vannamei was examined, and the toxic components in the cells were analyzed through high-pressure liquid chromatography (HLPC). In addition, the immune response of shrimp to the M. aeruginosa cell extract was assessed by measuring the activity of immune-related enzymes, as well as the transcription of the relevant genes. The results showed that M. aeruginosa cells, extract and cell-free cultured medium resulted in a 100%, 98.3%, and 1.7% mortality rate in shrimp, respectively. HPLC analysis results revealed the presence of microcystin-LR (MC-LR) at a concentration of 190.40 mg/kg of cells. In addition, the activity and gene transcription of two immune related enzymes, SOD and LZM, were both significantly reduced in shrimp hepatopancreas (p < 0.05) after injection with extract. However, reduced glutathione (GSH) content was slightly increased, but the ratio of GSH to GSSG decreased. The transcription of gst gene function as detoxification, was significantly downregulated (p < 0.05). The results demonstrated that M. aeruginosa cell extract was highly toxic to L. vannamei, and exerted a negative effect on shrimp immunity including reduction of antioxidant capacity, antibacterial activity and detoxification activity, due to toxins including microcystin-LR.

Combination inhibition of triple-negative breast cancer cell growth with CD36 siRNA-loaded DNA nanoprism and genistein.

Currently, a single treatment is less effective for triple-negative breast cancer (TNBC) therapy. Additionally, there are some limitations to the use of siRNA alone as a new method to treat breast cancer, such as its effective delivery into cells. In this study, we proposed a strategy that combines a siRNA-loaded DNA nanostructure and genistein for TNBC therapy. Both CD36 siRNA-loaded self-assembled DNA nanoprisms (NP-siCD36) and genistein knocked down CD36, resulting in enhanced anticancer efficacy through phosphorylation of the p38 MAPK pathway.In vitrostudies showed that combination therapy could effectively enhance cell apoptosis and reduce cell proliferation, achieving an antitumor effect in TNBC cells. The current study suggests that NP-siCD36 combined with genistein might be a promising strategy for breast cancer and treatment.

A tongue features fusion approach to predicting prediabetes and diabetes with machine learning.

BACKGROUND: Diabetics has become a serious public health burden in China. Multiple complications appear with the progression of diabetics pose a serious threat to the quality of human life and health. We can prevent the progression of prediabetics to diabetics and delay the progression to diabetics by early identification of diabetics and prediabetics and timely intervention, which have positive significance for improving public health. OBJECTIVE: Using machine learning techniques, we establish the noninvasive diabetics risk prediction model based on tongue features fusion and predict the risk of prediabetics and diabetics. METHODS: Applying the type TFDA-1 Tongue Diagnosis Instrument, we collect tongue images, extract tongue features including color and texture features using TDAS, and extract the advanced tongue features with ResNet-50, achieve the fusion of the two features with GA_XGBT, finally establish the noninvasive diabetics risk prediction model and evaluate the performance of testing effectiveness. RESULTS: Cross-validation suggests the best performance of GA_XGBT model with fusion features, whose average CA is 0.821, the average AUROC is 0.924, the average AUPRC is 0.856, the average Precision is 0.834, the average Recall is 0.822, the average F1-score is 0.813. Test set suggests the best testing performance of GA_XGBT model, whose average CA is 0.81, the average AUROC is 0.918, the average AUPRC is 0.839, the average Precision is 0.821, the average Recall is 0.81, the average F1-score is 0.796. When we test prediabetics with GA_XGBT model, we find that the AUROC is 0.914, the Precision is 0.69, the Recall is 0.952, the F1-score is 0.8. When we test diabetics with GA_XGBT model, we find that the AUROC is 0.984, the Precision is 0.929, the Recall is 0.951, the F1-score is 0.94. CONCLUSIONS: Based on tongue features, the study uses classical machine learning algorithm and deep learning algorithm to maximum the respective advantages. We combine the prior knowledge and potential features together, establish the noninvasive diabetics risk prediction model with features fusion algorithm, and detect prediabetics and diabetics noninvasively. Our study presents a feasible method for establishing the association between diabetics and the tongue image information and prove that tongue image information is a potential marker which facilitates effective early diagnosis of prediabetics and diabetics.

ESIPT-based fluorescent probe for bioimaging and identification of group IIIA ions in live cells and zebrafish.

Aluminum (Al), gallium (Ga), indium (In) are three essential elements in group IIIA of the periodic table, which all share similar chemical properties and are also vital in many aspects of bio- and environmental systems. Proper control of their levels is thus necessary as overexposure to them has been linked to onsets of many diseases. Fluorescence based molecular probes have always been the driving horse for detecting vital ions including group IIIA ions. However, only a few such probes have been reported so far and all of them are faced with one or more shortcomings such as not very high sensitivity, incapability to detect multiple ions simultaneously, and poor cell penetration abilities due to emitted fluorescence at shorter wavelengths. To meet those challenges, we herein presented the successful development and application of a novel group IIIA ions fluorescent probe, NBD-hnap, in live RAW264.7 cell and zebrafish models, especially the imaging of ocular tumor cell OCM-1 (human choroid melanoma cells). NBD-hnap was synthesized by a simple conjugation of NBD and hnap molecules under suitable conditions. Subsequent experimental analysis and theoretical calculations confirmed that NBD-hnap forms a 1:1 chelate with each of three selected group IIIA ions. Further evaluation proved that NBD-hnap can realize highly sensitive [LODs of 113, 82 and 150 nM for Al(III), Ga(III), and In(III) respectively in aqueous solutions] and highly selective (over a dozen of interfering cations) through an ESIPT-based fluorescent sensing mechanism with strong far-red emission around 640 nm. Those value merits make NBD-hnap superior to other group IIIA ion probes reported before and NBD-hnap is thus expected to find wider and greater applications in the near future.

Tongue image quality assessment based on a deep convolutional neural network.

BACKGROUND: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Therefore, an automatic, efficient and accurate quality control model is of significance to the development of intelligent tongue diagnosis technology for TCM. METHODS: Machine learning methods, including Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (Adaboost), Naïve Bayes, Decision Tree (DT), Residual Neural Network (ResNet), Convolution Neural Network developed by Visual Geometry Group at University of Oxford (VGG), and Densely Connected Convolutional Networks (DenseNet), were utilized to identify good-quality and poor-quality tongue images. Their performances were made comparisons by using metrics such as accuracy, precision, recall, and F1-Score. RESULTS: The experimental results showed that the accuracy of the three deep learning models was more than 96%, and the accuracy of ResNet-152 and DenseNet-169 was more than 98%. The model ResNet-152 obtained accuracy of 99.04%, precision of 99.05%, recall of 99.04%, and F1-score of 99.05%. The performances were better than performances of other eight models. The eight models are VGG-16, DenseNet-169, SVM, RF, GBDT, Adaboost, Naïve Bayes, and DT. ResNet-152 was selected as quality-screening model for tongue IQA. CONCLUSIONS: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and indicate that applying deep learning methods, specifically deep CNNs, to evaluate poor-quality tongue images is feasible.

Clinical data mining on network of symptom and index and correlation of tongue-pulse data in fatigue population.

BACKGROUND: Fatigue is a kind of non-specific symptom, which occurs widely in sub-health and various diseases. It is closely related to people's physical and mental health. Due to the lack of objective diagnostic criteria, it is often neglected in clinical diagnosis, especially in the early stage of disease. Many clinical practices and researches have shown that tongue and pulse conditions reflect the body's overall state. Establishing an objective evaluation method for diagnosing disease fatigue and non-disease fatigue by combining clinical symptom, index, and tongue and pulse data is of great significance for clinical treatment timely and effectively. METHODS: In this study, 2632 physical examination population were divided into healthy controls, sub-health fatigue group, and disease fatigue group. Complex network technology was used to screen out core symptoms and Western medicine indexes of sub-health fatigue and disease fatigue population. Pajek software was used to construct core symptom/index network and core symptom-index combined network. Simultaneously, canonical correlation analysis was used to analyze the objective tongue and pulse data between the two groups of fatigue population and analyze the distribution of tongue and pulse data. RESULTS: Some similarities were found in the core symptoms of sub-health fatigue and disease fatigue population, but with different node importance. The node-importance difference indicated that the diagnostic contribution rate of the same symptom to the two groups was different. The canonical correlation coefficient of tongue and pulse data in the disease fatigue group was 0.42 (P < 0.05), on the contrast, correlation analysis of tongue and pulse in the sub-health fatigue group showed no statistical significance. CONCLUSIONS: The complex network technology was suitable for correlation analysis of symptoms and indexes in fatigue population, and tongue and pulse data had a certain diagnostic contribution to the classification of fatigue population.

GC-PROM: validation of a patient-reported outcomes measure for Chinese patients with gastric cancer.

BACKGROUND: There is increasing recognition that PROs are important in the estimation of the burden of long-term survival among patients with gastric cancer. The study aimed to develop a disease-specific instrument to assess patient-reported outcomes for Chinese patients with gastric cancer. METHOD: Following the FDA's draft guidance for patient-reported outcome, conceptual framework and item pool were defined based on relevant existing work. A draft scale was formed after revising some items based on feedback from experts and Chinese patients with gastric cancer. The pre-survey and formal survey were conducted in eight different hospitals in Shanxi Province, and two item-selection process based on classical test theory and item response theory. Finally, the patient-reported outcomes measure for Chinese patients with gastric cancer (GC-PROM) was validated in terms of reliability, validity, and feasibility. The minimal clinically important difference was determined by distribution-based method. RESULTS: The final GC-PROM consisted of 38 items, 13 subdomains, and 4 domains. Reliability was verified by Cronbach's alpha coefficient for four domains and 13 subdomains respectively. The validity results showed that the multidimensional scale fulfilled expectations. In the formal survey, the completion rate was 96.16%, and the average filling time was less than half an hour. The values of the minimal clinically important difference were 4.14, 3.41, 3.37, and 3.28 in the four domains. CONCLUSIONS: The GC-PROM had good reliability, validity, and feasibility and thus can be considered an effective clinical evaluation instrument for Chinese patients with gastric cancer.

Benchmarking polarizable and non-polarizable force fields for Ca2+-peptides against a comprehensive QM dataset.

Explicit description of atomic polarizability is critical for the accurate treatment of inter-molecular interactions by force fields (FFs) in molecular dynamics (MD) simulations aiming to investigate complex electrostatic environments such as metal-binding sites of metalloproteins. Several models exist to describe key monovalent and divalent cations interacting with proteins. Many of these models have been developed from ion-amino-acid interactions and/or aqueous-phase data on cation solvation. The transferability of these models to cation-protein interactions remains uncertain. Herein, we assess the accuracy of existing FFs by their abilities to reproduce hierarchies of thousands of Ca2+-dipeptide interaction energies based on density-functional theory calculations. We find that the Drude polarizable FF, prior to any parameterization, better approximates the QM interaction energies than any of the non-polarizable FFs. Nevertheless, it required improvement in order to address polarization catastrophes where, at short Ca2+-carboxylate distances, the Drude particle of oxygen overlaps with the divalent cation. To ameliorate this, we identified those conformational properties that produced the poorest prediction of interaction energies to reduce the parameter space for optimization. We then optimized the selected cation-peptide parameters using Boltzmann-weighted fitting and evaluated the resulting parameters in MD simulations of the N-lobe of calmodulin. We also parameterized and evaluated the CTPOL FF, which incorporates charge-transfer and polarization effects in additive FFs. This work shows how QM-driven parameter development, followed by testing in condensed-phase simulations, may yield FFs that can accurately capture the structure and dynamics of ion-protein interactions.

Two new xanthones, a new lignin, and twenty phenolic compounds from Smilax china and their NO production inhibitory activities.

Two new xanthones smilone A (1), smilone B (2), and a new lignin smilgnin A (3) were isolated from the rhizomes of Smilax china L., together with three known xanthones (4-6), four lignins (7-10), two flavones (11, 12), two stilbenoids (13, 14), and ten organic phenoloids (15-24). Of them, compounds 4-6 were isolated from the genus Smilax for the first time. The structures of 1-24 were elucidated by the extensive analysis of spectral data and compared with the literature. All compounds were evaluated for their inhibitory effects against LPS-induced NO production in RAW264.7 macrophages. Among them, compound 24 exhibited significant inhibitory activity against NO production (IC50 = 1.26 µM), while compounds 3, 6, and 7 showed weak activities at the concentration of 50 µM.[Formula: see text].

Contamination of antibiotic resistance genes (ARGs) in a typical marine aquaculture farm: source tracking of ARGs in reared aquatic organisms.

Although the prevalence and concentrations of antibiotic resistance genes (ARGs) in aquaculture is receiving increasing scientific interest, there is little understanding of the direct sources and dissemination pathways of ARGs in marine aquaculture-reared organisms. This study investigated the dynamics of ARGs and the bacterial community throughout the rearing period in a typical marine aquaculture farm in South China. The results demonstrated that sul1 and qnrD were predominant in the sediment, and qnrD and qnrA were predominant in the intestinal tracts of shrimps. Network analysis showed that the chemical oxygen demand, total organic carbon, dissolved organic carbon, suspended solids, and total phosphorus were positively correlated with the predominant ARGs. The results of the network and source tracking analyses indicate that environmental factors and the bacterial community may drive the dissemination of ARGs dissemination in the environment and in shrimp reared by marine aquaculture, and sediment is the most direct and important medium in this dissemination. These results aid in improving our understanding of the sources, level, and dissemination of ARGs in marine aquaculture.

ERß modulates genistein's cisplatin-enhancing activities in breast cancer MDA-MB-231 cells via P53-independent pathway.

As one of the typical food-derived phytoestrogens, genistein (GEN) could bind to estrogen receptor (ER) and was reported to be closely related to breast cancer. Our former research showed that GEN interfered with the anti-tumor effects of cisplatin (CIS) in breast cancer MCF-7 (ERα+/ERß-) cells. However, it is not clear whether ER expression pattern affects GEN's modulation on CIS's activity. In the present study, breast cancer ERß knockdown (ERßKD) MDA-MB-231 (ERα-/ERß+) cell model was established via ERß RNAi lentivirus infection. The role of ERß expression in GEN's bioeffects on cells' response to CIS was investigated and was further double-checked by pathway-specific inhibitor PHTPP. Consistent results were harvested through cell viability analysis, cell cycle distribution flow cytometry, TUNEL staining, and expression detection of key biomarkers, Bax, Bcl-2, P21, P53, and cleaved caspase-3. Compared with the control group, PHTPP-treated or ERßKD cells exhibited higher sensitivity to both GEN and CIS treatment. GEN and CIS showed synergistic effects only in ERß-deficient cells. This effect mainly resulted in G2 phase arresting and apoptosis induction with the upregulation of P21 and Bax/Bcl-2 protein level. Besides, P53 expression was strikingly suppressed in ERß-deficient cells. This indicated ERß pathway deficiency might enhance GEN-CIS bioactivity via the downregulation of P53. In summary, our data imply that daily intake of GEN-rich diet could collaborate with CIS anti-tumor treatment in ERα-/ERß- breast cancer cases. ERß pathway might be one of the potential targets which elicit GEN's positive effects in ERα- breast cancer patients.

Abundance and removal of antibiotic resistance genes (ARGs) in the rearing environments of intensive shrimp aquaculture in South China.

Although research regarding antibiotic resistance genes (ARGs) in aquaculture environments has gained increasing scientific interest, further studies are required to understand the abundances and removal mechanisms of ARGs during the entire rearing period of shrimp aquaculture. Thus, in this study, abundances, distributions and removal rates of ARGs in different environmental compartments of intensive shrimp farms in South China were investigated during the entire rearing period. The results indicated that sul1 and cmlA were the predominant ARGs in the water and sediment samples. Additionally, the total abundance of ARGs was higher in shrimp pond water than in the source water and farm effluent. Moreover, sediment samples indicated significantly higher ARG abundances than water samples from the shrimp ponds (P < 0.05). Environmental factors were found to significantly affect the distribution of ARGs in shrimp rearing environments. Furthermore, stable ponds aided the removal of ARGs from shrimp pond water. This study accounted for temporal variations in ARG abundances as well as removal of ARGs in different environmental compartments during the entire shrimp rearing period. However, additional research is required to optimize the water treatment process for removal of ARGs from the aquaculture.

miR-33 and RIP140 participate in LPS-induced acute lung injury

Background/aim: Pulmonary microvascular endothelial cells (PMVECs) play a pivotal role in the process of acute lung injury (ALI), which can be induced by lipopolysaccharide (LPS). Numerous reports have indicated that both miR-33 and RIP140 are involved in the inflammatory response in macrophages. In this study, we sought to investigate whether miR-33 and RIP140 participate in ALI induced by LPS. Materials and methods: First, we isolated and identified PMVECs from BALB/c mice. Subsequently, both PMVECs and BALB/c mice were treated with PBS, LPS, or pyrrolidine dithiocarbamate (PDTC) plus LPS and divided into three groups: control (PBS), LPS (LPS), and L+P (LPS plus PDTC) groups. We assessed pathology by hematoxylin and eosin staining, and miR-33 and RIP140 expression levels were examined using quantitative PCR and Western blot analyses. Results: Our results demonstrated that LPS can induce PMVEC injury and ALI and that LPS treatment significantly decreased miR-33 expression compared with controls (P < 0.001, n = 5). On the contrary, RIP140 was markedly overexpressed by LPS treatment (P < 0.001, n = 5). However, this alteration can be inhibited by pretreatment with PDTC before LPS (P < 0.05, n = 5). Conclusion: This study is the first to confirm that both miR-33 and RIP140 participate in LPS-induced PMVEC injury and ALI, which may help uncover the mechanism of ALI