Radioactive iodine therapy for thyroid cancer coexisting with hashimoto's thyroiditis: a systematic review and meta-analysis.

OBJECTIVES: To investigate the clinical character of differentiated thyroid cancer (DTC) coexisting with Hashimoto's thyroiditis (HT) and provide state-of-art evidence for personalized RAIT for patients coexisting with HT. METHODS: From January 2000 to January 2023, PubMed, Embase, Web of Science databases were searched for relevant original articles that published in English on the RAIT efficacy for DTC with HT. Revman 5.4 and Stata 17.0 were used for date analysis. RESULTS: Eleven studies involved 16,605 DTC patients (3,321 with HT) were included. HT were more frequent in female (OR: 2.90, 95% CI: 1.77 to 4.76, P < 0.00001). The size of tumor (MD: -0.20, 95% CI: -0.30 to -0.11), extrathyroidal extension rate (OR: 0.77, 95% CI: 0.67 to 0.90) and metastasis rate (OR: 0.18, 95% CI: 0.08 to 0.41) were less in HT, but TNM stage had no significant difference among HT and non-HT group. DFS rate (OR: 1.96, 95% CI : 1.57 to 2.44, P < 0.00001), 5-year and 10-year DFS (OR: 1.73, 95% CI: 1.04 to 2.89, P = 0.04; OR: 1.56, 95% CI: 1.17 to 2.09, P = 0.003, respectively) were higher in HT group. The recurrent (OR: 0.62, 95% CI: 0.45 to 0.83, P = 0.002), RAIT dosage (MD=-38.71, 95% CI: -60.86 to -16.56, P = 0.0006) and treatment (MD: -0.13, 95% CI: -0.22 to -0.03, P = 0.008) were less in HT group. CONCLUSIONS: DTC coexisting with HT was associated with less invasion. DFS of HT group was higher than non-HT group after RAIT. Low dose treatment did not impair the efficacy of RAIT in DTC with HT. ADVANCES IN KNOWLEDGE: Hashimoto's thyroiditis is a risk for DTC, but it minimalizes the progression of cancer and enhance the efficacy of RAIT, which should be considered in personalizing RAIT.

Residents' willingness to engage in carbon generalized system of preferences - A personality insight study based on the extended theory of planned behavior.

In the context of China's dual carbon target, reducing personal carbon emissions has been identified as a crucial strategy to achieve the target. The 2022 Digital China Development Report emphasizes the significance of implementing the Carbon Generalized System of Preferences (CGSP) in driving individual carbon reduction efforts in China. However, the psychological factors influencing public participation in CGSP are still unknown. Based on the Extended Theory of Planned Behavior (TPB), this study explored the psychological factors of different personality trait groups' participation in the CGSP and categorized 712 respondents into Compatible, Positive, Responsible, and Susceptible based on the MINI-IPIP scale and the K-means method. The results show that the strength of willingness to engage (WTE) in CGSP was ranked as: Compatible > Positive > Responsible > Susceptible and the WTE of compatible groups is more influenced by attitude, while Perceived Behavioral Control (PBC) plays a more crucial role in other groups. Personal Norms (PN) and Policy Awareness (PA) were significant for all specific personality groups except the Susceptible group. Surprisingly subjective norms had little to do with WTE. We believe that policymakers should consider the impact of PBC on WTE when formulating policies and raise the expectation of residents in terms of the value they can obtain from participating in CGSP. Additionally, promotional activities related to PN and PA in connection with CGSP should be conducted. These efforts may help individuals better engage in CGSP.

HSD17B13 liquid-liquid phase separation promotes leukocyte adhesion in chronic liver inflammation.

The rs72613567:TA polymorphism in 17-beta hydroxysteroid dehydrogenase 13 (HSD17B13) has been found to reduce the progression from steatosis to nonalcoholic steatohepatitis. In this study, we sought to define the pathogenic role of HSD17B13 in triggering liver inflammation. Here we find that HSD17B13 forms liquid-liquid phase separation (LLPS) around lipid droplets in the livers of nonalcoholic steatohepatitis patients. The dimerization of HSD17B13 supports the LLPS formation and promotes its enzymatic function. HSD17B13 LLPS increases the biosynthesis of platelet activating factor (PAF), which in turn promotes fibrinogen synthesis and leukocyte adhesion. Blockade of PAFR or STAT3 pathway inhibited the fibrinogen synthesis and leukocyte adhesion. Importantly, adeno-associated viral-mediated xeno-expression of human HSD17B13 exacerbated western diet/carbon tetrachloride-induced liver inflammation in Hsd17b13-/- mice. In conclusion, our results suggest that HSD17B13 LLPS triggers liver inflammation by promoting PAF-mediated leukocyte adhesion, and targeting HSD17B13 phase transition could be a promising therapeutic approach for treating hepatic inflammation in chronic liver disease.

Radiating blood flow signal: A new ultrasound feature of thyroid carcinoma.

OBJECTIVE: To summary radiating blood flow signals and evaluate their diagnostic value in differentiating benign and malignant thyroid nodules. MATERIALS AND METHODS: We retrospectively recruited consecutive patients undergoing US at 4 hospitals from 2018 to 2022. In a training dataset, the correlations of US features with malignant thyroid nodules were assessed by multivariate logistic analysis. Multivariate logistic regression models involving the ACR TI-RADS score, radiating blood flow signals and their combination were built and validated internally and externally. The AUC with 95% asymptotic normal confidence interval as well as sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) with 95% exact binomial confidence intervals were calculated. RESULTS: Among 2475 patients (1818 women, age: 42.47 ± 11.57; 657 men, age: 42.16 ± 11.69), there were 3187 nodules (2342 malignant nodules and 845 benign nodules). Radiating blood flow signals were an independent risk factor for diagnosing thyroid carcinoma. In the training set, the AUC of the model using the combination of radiating blood flow signals and the ACR TI-RADS score (0.95 95 % CI: [0.94, 0.97]; P < 0.001) was significantly higher than that of the ACR TI-RADS model (0.91 [0.89, 0.93]). In the two internal validation sets and the external validation set, the AUCs of the combination model were 0.97 [0.96, 0.98], 0.92 [0.88, 0.96], and 0.91 [0.86, 0.95], respectively, and were all significantly higher than that of the ACR TI-RADS score (0.92 [0.90, 0.95], 0.86 [0.81, 0.91], 0.84 [0.79, 0.89]; P < 0.001). CONCLUSION: Radiating blood flow is a new US feature of thyroid carcinomas that can significantly improve the diagnostic performance vs. the ACR TI-RADS score.

Comparison of drug-eluting bead with conventional transcatheter arterial chemoembolization for hepatocellular carcinoma with portal vein tumor thrombus: A randomized clinical trial.

BACKGROUND: Drug-eluting bead transarterial chemoembolization (DEB-TACE) has shown efficacy for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). However, whether DEB-TACE is superior to conventional TACE (cTACE) remains unclear. OBJECTIVE: This randomized controlled trial aimed to compare the efficacy and safety of DEB-TACE versus cTACE in treating HCC with PVTT. METHODS: The study was conducted in a tertiary care center in Southeast China. HCC patients with PVTT were randomized at a 1:1 ratio to the DEB-TACE or cTACE groups. The primary endpoint was progression-free survival (PFS), and the secondary endpoints were overall survival (OS) and incidence of adverse events (AEs). An independent review committee assessed the radiologic response according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). AEs were assessed by the Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Systemic therapies were not limited. RESULTS: Between September 2018, and July 2020, 163 patients were randomized to undergo DEB-TACE (n=82) or cTACE (n=81). Nine patients were excluded, and 154 patients were included in the final analysis; the median age was 55 years (range, 24-78 y), and 140 (90.9%) were male. The median PFS in the DEB-TACE group was 6.0 months (95% CI, 5.0 to 10.0) versus 4.0 months (95% CI, 3.0 to 5.0) in the cTACE group (hazard ratio, 0.63; 95% CI, 0.42 to 0.95; P=0.027). The DEB-TACE group showed a higher response rate (51[66.2%] vs. 36 [46.8%]; P=0.0015) and a longer median OS (12.0 months [95% CI, 9.0 to 16.0] vs. 8.0 months [95% CI, 7.0 to 11.0], P=0.039) than the cTACE group. Multivariate analysis showed that the treatment group, ALBI score, distant metastasis and additional TKIs were the four independent prognostic factors correlated with PFS. In addition, the treatment group, PVTT group and combined with surgery were independently correlated with OS. AEs were similar in the two groups, and postembolization syndrome was the most frequent AEs. CONCLUSION: DEB-TACE is superior to cTACE in treating HCC patients with PVTT due to the improved PFS and OS with an acceptable safety profile and may become a promising treatment strategy for HCC patients with PVTT. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1800018035.

Mechanism of Mulberry Leaves and Black Sesame in Alleviating Slow Transit Constipation Revealed by Multi-Omics Analysis.

Traditional Chinese medicine (TCM) possesses the potential of providing good curative effects with no side effects for the effective management of slow transit constipation (STC), an intestinal disease characterized by colonic dyskinesia. Mulberry leaves (Morus alba L.) and black sesame (Sesamum indicum L.), referred to as SH, are processed and conditioned as per standardized protocols. SH has applications as food and medicine. Accordingly, we investigated the therapeutic potential of SH in alleviating STC. The analysis of SH composition identified a total of 504 compounds. The intervention with SH significantly improved intestinal motility, reduced the time for the first black stool, increased antioxidant activity, and enhanced water content, thereby effectively alleviating colon damage caused by STC. Transcriptome analysis revealed the SH in the treatment of STC related to SOD1, MUC2, and AQP1. The analysis of 16S rRNA gene sequences indicated notable differences in the abundance of 10 bacteria between the SH and model. Metabolomic analysis further revealed that SH supplementation increased the levels of nine metabolites associated with STC. Integrative analysis revealed that SH modulated amino acid metabolism, balanced intestinal flora, and targeted key genes (i.e., SOD1, MUC2, AQP1) to exert its effects. SH also inhibited the AQP1 expression and promoted SOD1 and MUC2 expression.

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

Heterogeneous integration of an on-chip Nd:YAG whispering gallery mode laser with a lithium-niobate-on-insulator platform.

The integration of heterogeneous optical components onto an optical platform is crucial for the advancement of photonic chips. To achieve this, efficient coupling of optical signals between components and the platform is essential. Here, we have successfully integrated a Nd:YAG microdisk laser with a lithium-niobate-on-insulator (LNOI) photonic platform by modulating the propagation modes of LNOI. Ridge waveguides are fabricated on the LNOI by carefully adjusting the cross-sectional dimensions to enable the propagation of higher-order propagation modes. This ridge waveguide ensures that the effective refractive index of the higher-order mode closely matches that of the fundamental mode of the Nd:YAG microdisk, ensuring efficient waveguide-microdisk coupling. This on-chip laser, consisting of an Nd:YAG microdisk and LNOI integration, achieves a maximum output power of 23 µW, and a mode suppression ratio of 53.6 dB. This research presents an efficient approach for constructing highly functional heterogeneous integrated optical chips.

Low energy consumption layout of exhibition buildings in Yangtze River Delta region.

The issue of high energy consumption in exhibition buildings has long been a focal point in the field of architectural design. However, current energy consumption assessments for exhibition buildings mainly focus on post-construction evaluations, lacking corresponding guidance during the initial design phase. To address this issue, this study selected 48 well-known exhibition buildings both domestically and internationally as research subjects. Utilizing scatter plot curve fitting, it was discovered that there exists a nonlinear quadratic relationship between the building area of the first floor and the courtyard area. Based on this relationship, four typical layouts were established to match the climatic characteristics of Hangzhou, a representative region in the Yangtze River Delta of China. Taking into account regional architectural features, the study specifically examined the impact of different orientations and window-to-wall ratios on energy consumption levels. The influence of these factors on energy consumption was analyzed using the DesignBuilder software. The results revealed that there exists an optimal window-to-wall ratio for exhibition buildings, with parallel, L-shaped enclosed south-facing courtyards, and U-shaped enclosed east-facing courtyards showing greater energy efficiency. This research provides guidance for designing exhibition buildings that are energy-efficient and foster a harmonious indoor-outdoor relationship.

Selected legacy and emerging organic contaminants in sediments of China's Yangtze - the world's third longest river: Response to anthropogenic activities.

To explore contaminant concerns as a result of anthropogenic disturbance of the river system, this study provided the first extensive investigation of the contamination profiles, possible driving factors, and ecological risks of 40 target compounds including pharmaceuticals and personal care products (PPCPs), neonicotinoid pesticides (NNIs), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs) in sediments of the whole Yangtze River (the world's third longest river). Among these target compounds, PPCPs were the dominant contaminants with a total concentration (∑15PPCPs) of 2.13-14.99 ng/g, followed by ∑7PCBs (

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI-assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. METHODS: This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). RESULTS: A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. CONCLUSION: The combined model and nomogram, which integrate pathomics multi-modal data and clinical-pathological variables, demonstrated superior performance in distinguishing between Stage I-II and Stage III colorectal cancer. This provides valuable support for clinical decision-making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer.

OBJECTIVE: This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). METHODS: This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). RESULTS: A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. CONCLUSION: The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

MedChatZH: A tuning LLM for traditional Chinese medicine consultations.

Generative Large Language Models (LLMs) have achieved significant success in various natural language processing tasks, including Question-Answering (QA) and dialogue systems. However, most models are trained on English data and lack strong generalization in providing answers in Chinese. This limitation is especially evident in specialized domains like traditional Chinese medical QA, where performance suffers due to the absence of fine-tuning and high-quality datasets. To address this, we introduce MedChatZH, a dialogue model optimized for Chinese medical QA based on transformer decoder with LLaMA architecture. Continued pre-training on a curated corpus of Chinese medical books is followed by fine-tuning with a carefully selected medical instruction dataset, resulting in MedChatZH outperforming several Chinese dialogue baselines on a real-world medical dialogue dataset. Our model, code, and dataset are publicly available on GitHub (https://github.com/tyang816/MedChatZH) to encourage further research in traditional Chinese medicine and LLMs.

Remazolam affects the phenotype and function of astrocytes to improve traumatic brain injury by regulating the Cx43.

PURPOSE: To explore the mechanism by which Remazolam affects the phenotype and function of astrocytes to improve traumatic brain injury (TBI). METHODS: The oxygen -glucose deprivation/recovery (OGD/R) cell model was constructed to simulate the pathological state of astrocytes in a TBI environment. The viability of astrocytes was measured by CCK-8, and the cytoskeleton changes were observed by Phalloidin- TRITC staining. The expressions of differentiation markers, Cx43 and phosphorylated Cx43 (P-Cx43) of A1/A2 astrocytes were detected by Western blot, and the complement C3 and S100A10 of A1/A2 astrocytes were detected by ELISA. The TBI rat model was established. The water content of brain tissue was measured by dry-wet specific gravity method, the pathological morphology of brain tissue in cortical injury area was observed by HE staining method, ROS was detected by fluorescence quantitative method, Cx43 expression was detected by immunohistochemistry method, and the differentiation markers of A1/A2 astrocytes were detected by immunofluorescence. RESULTS: In the TBI environment, astrocytes showed decreased cell viability, blurred skeleton, and increased expression of Cx43. In TBI rats, the water content of brain tissue increased, the brain tissue in the cortex injury area was seriously damaged, ROS and Cx43 expression were significantly increased, and mainly distributed in A2 astrocytes. Remazolam can reverse the above results after the intervention. CONCLUSION: Remazolam affects the phenotype and function of astrocytes to improve TBI via regulating Cx43, and plays a role in protecting the neurological function of TBI rats.

Analysis of the potential application of a residential composite energy storage system based on a double-layer optimization model.

Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.

Biomimetic construction of environmental-tolerant composite hydrogels based on galactomannan for tough, flexible and conductive sensors.

Sustainable composite hydrogel materials with harsh environmental adaption and tolerance capability have received considerable interests but still remain as challenges. In this work, biomimetic strategy was adapted for construction of three-dimensional galactomannan (GM) hydrogels with intercalation of flexible polymer chains polyethyleneimine (PEI), biomacromolecules tannin acid (TA) and CeO2 nanoparticles (NPs). The hydrogels cross-linked with double-networks (DN) present not only pH-responsive water absorption property, but also boosted mechanical strength with highest toughness of 326 kJ/m3 and Young's modulus of 220 kPa. Self-healing and anti-freezing capabilities were revealed for the hydrogels by maintaining of fracture elongation (23 %) and fracture strength (250 kPa). TA, CeO2 NPs as well as the amide groups in PEI of the hydrogels introduced excellent bacterial prohibition performance on both Bacillus subtilis (B. subtilis) and Escherichia coli (E. coli). Also, due to the existence of the free ions, the hydrogels exhibited electric conductive properties, with wide-range high sensitivity and long-time conductive stability. In addition, various tensile strain degrees were related to the conductive resistance values, and the great recovery performance was proved by cyclic tensile-conductive tests for 3000 times. Therefore, the proposed GM-based hydrogels displayed great potentials as strain sensors that are adaptable and tolerant to various environmental conditions.

Transferability-Guided Cross-Domain Cross-Task Transfer Learning.

We propose two novel transferability metrics fast optimal transport-based conditional entropy (F-OTCE) and joint correspondence OTCE (JC-OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more generalizable representations for cross-domain cross-task transfer learning. Unlike the original OTCE metric that requires evaluating the empirical transferability on auxiliary tasks, our metrics are auxiliary-free such that they can be computed much more efficiently. Specifically, F-OTCE estimates transferability by first solving an optimal transport (OT) problem between source and target distributions and then uses the optimal coupling to compute the negative conditional entropy (NCE) between the source and target labels. It can also serve as an objective function to enhance downstream transfer learning tasks including model finetuning and domain generalization (DG). Meanwhile, JC-OTCE improves the transferability accuracy of F-OTCE by including label distances in the OT problem, though it incurs additional computation costs. Extensive experiments demonstrate that F-OTCE and JC-OTCE outperform state-of-the-art auxiliary-free metrics by 21.1% and 25.8% , respectively, in correlation coefficient with the ground-truth transfer accuracy. By eliminating the training cost of auxiliary tasks, the two metrics reduce the total computation time of the previous method from 43 min to 9.32 and 10.78 s, respectively, for a pair of tasks. When applied in the model finetuning and DG tasks, F-OTCE shows significant improvements in the transfer accuracy in few-shot classification experiments, with up to 4.41% and 2.34% accuracy gains, respectively.

Occurrence, source apportionment and risk assessment of perfluorinated compounds in sediments from the longest river in Asia.

This study investigated the pollution of perfluorinated compounds (PFCs) in sediments from the main stream of the Yangtze River, the longest river in Asia. Totally, 13 of 15 PFASs were detected in the sediments and the total concentrations ranged from 0.058 ng/g to 0.89 ng/g dry weight (dw), with dominant contaminants by perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA). Concentrations of PFASs in the downstream were higher than those of upstream and midstream. Four main sources were analysed using the Unmix model, textile treatments and food packaging dominantly accounted for approximately half of the total sources, followed by metal electroplating (26.8%), fluoropolymer products (16.3%) and fluororesin coatings (7.4%). Total organic carbon (TOC), total nitrogen (TN) and grain size had significant correlation with the concentration of PFASs in sediments, indicating that the physical and chemical parameters could directly affect the adsorption process of PFASs. In addition, anthropogenic factors such as urbanization rate and per capita GDP also had a direct impact on the distribution of PFASs. Environmental risk assessment showed that PFOS posed low to medium risks to the Yangtze River, indicating that sustained attentions were needed.

Ultrasound Microbubble-Stimulated miR-145-5p Inhibits Malignant Behaviors of Breast Cancer Cells by Targeting ACTG1.

ABSTRACT: Ultrasound-targeted microbubble destruction (UTMD) technology combines ultrasound with a variety of functional microbubble vectors to enhance the transfection and expression of target genes, and has become a promising noninvasive method for localized gene transfer, which is widely used in gene therapy for cancer. This research aimed to explore the role of UTMD-mediated miR-145-5p on breast cancer (BC) tumorigenesis and the underlying mechanisms. To achieve UTMD-mediated miR-145-5p overexpression, BC cells were cotransfected with microbubbles (MBs) and miR-145-5p mimics. The BC cell malignant phenotypes were assessed through CCK-8, wound healing, and transwell assays. MiR-145-5p and actin gamma 1 (ACTG1) binding relationship was verified through luciferase reporter and RNA pull-down assays. MiR-145-5p and ACTG1 levels in BC cells and tissues were detected through RT-qPCR and Western blotting. ACTG1 was upregulated, whereas miR-145-5p was downregulated in BC cells and tissues. MiR-145-5p targeted ACTG1 and negatively regulated its level in BC cells. Overexpressing miR-145-5p restrained BC cell growth, migration, and invasion. Ultrasound-targeted microbubble destruction improved the overexpression efficiency of miR-145-5p and enhanced the suppressive influence on BC cell malignant phenotypes. In addition, ACTG1 overexpression compromises the repression of UTMD-mediated miR-145-5p on cellular behaviors in BC. Ultrasound-targeted microbubble destruction-delivered miR-145-5p hindered malignant behaviors of BC cells through downregulating ACTG1.