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Production of magnetic sodium alginate polyelectrolyte nanospheres for lead ions removal from wastewater

Wang J, Guo M, Luo Y, Shao D, Ge S, Cai L, et al.

J Environ Manage, 2021 Jul 01;289:112506.
PMID: 33831760 DOI: 10.1016/j.jenvman.2021.112506

Polyelectrolyte composite nanospheres are relatively new adsorbents which have attracted much attention for their efficient pollutant removal and reuse performance. A novel polyelectrolyte nanosphere with magnetic function (SA@AM) was synthesized via the electrostatic reaction between the polyanionic sodium alginate (SA) and the surface of a prepared terminal amino-based magnetic nanoparticles (AMs). SA@AM showed a size of 15-22 nm with 6.85 emu·g-1 of magnetization value, exhibiting a high adsorption capacity on Pb(II) ions representing a common heavy metal pollutant, with a maximum adsorption capacity of 105.8 mg g-1. The Langmuir isotherm adsorption fits the adsorption curve, indicating uniform adsorption of Pb(II) on the SA@AM surfaces. Repeated adsorption desorption experiments showed that the removal ratio of Pb(II) by SA@AM was more than 76%, illustrating improved regeneration performance. These results provide useful information for the production of bio-based green magnetic nano scale adsorption materials for environmental remediation applications.
Fulltext Determinants of Intention to Purchase Bottled Water Based on Business Online Strategy in China: The Role of Perceived Risk in the Theory of Planned Behavior

Guo M, Tan CL, Wu L, Peng J, Ren R, Chiu CH

Int J Environ Res Public Health, 2021 10 13;18(20).
PMID: 34682476 DOI: 10.3390/ijerph182010729

With the development of the network economy, especially the promotion and popularization of mobile networks, traditional offline businesses are further integrated with online businesses, promoting the development of business online strategies. However, with the growth of enterprises' business, their negative externalities on the environment have gradually become prominent, further affecting sustainable consumption. The relationships between businesses, the environment, and consumption have become the focus of attention. China's fast-growing bottled water companies face similar challenges. The pollution that occurs due to bottled water packaging poses great threats to consumers. Hence, this study extended the Theory of Planned Behavior (TPB) by integrating three risk aspects, namely, water pollution risk perception (WPRP), non-degradable package pollution risk perception (NPPRP), and false information risk perception (FIRP), to examine the consumers' perceptions toward these risk aspects before purchasing bottled water online. This study employed a cross-sectional approach to collect data from online consumers via a survey method. A total of 401 valid samples were collected and then analyzed via a structural equation model using the AMOS statistical package. The results showed that attitude (AT), subjective norm (SN), and perceived behavior control (PBC) toward online bottled water purchase had significant and positive effects on the consumers' purchase intentions (PIs). However, under the influence of risk perception, the consumers' attitudes, SNs and PBC became suppressed by WPRP, and SN became suppressed due to the impact of FIRP. Furthermore, the negative impacts of NPPRP and FIRP on PI were partially mediated by AT, SN and PBC. Meanwhile, WPRP imposed the most significant direct effect on PI. The study results will help businesses to develop better online strategies to reduce the risk perception of bottled water and provide theoretical value and practical guidance for realizing sustainable consumption.
Myocardial fibrosis induced by nonylphenol and its regulatory effect on the TGF-β1/LIMK1 signaling pathway

Guo M, Xu J, Long X, Liu W, Aris AZ, Yang D, et al.

Ecotoxicol Environ Saf, 2024 Mar 01;272:116110.
PMID: 38364763 DOI: 10.1016/j.ecoenv.2024.116110

OBJECTIVE: We here explored whether perinatal nonylphenol (NP) exposure causes myocardial fibrosis (MF) during adulthood in offspring rats and determined the role of the TGF-β1/LIMK1 signaling pathway in NP-induced fibrosis in cardiac fibroblasts (CFs).
METHODS AND RESULTS: Histopathology revealed increased collagen deposition and altered fiber arrangement in the NP and isoproterenol hydrochloride (ISO) groups compared with the blank group. Systolic and diastolic functions were impaired. Western blotting and qRT-PCR demonstrated that the expression of central myofibrosis-related proteins (collagens Ι and ΙΙΙ, MMP2, MMP9, TGF-β1, α-SMA, IL-1β, and TGF-β1) and genes (Collagen Ι, Collagen ΙΙΙ, TGF-β1, and α-SMA mRNA) was upregulated in the NP and ISO groups compared with the blank group. The mRNA-seq analysis indicated differential expression of TGF-β1 signaling pathway-associated genes and proteins. Fibrosis-related protein and gene expression increased in the CFs stimulated with the recombinant human TGF-β1 and NP, which was consistent with the results of animal experiments. According to the immunofluorescence analysis and western blotting, NP exposure activated the TGF-β1/LIMK1 signaling pathway whose action mechanism in NP-induced CFs was further validated using the LIMK1 inhibitor (BMS-5). The inhibitor modulated the TGF-β1/LIMK1 signaling pathway and suppressed the NP-induced increase in fibrosis-related protein expression in the CFs. Thus, the aforementioned pathway is involved in NP-induced fibrosis.
CONCLUSION: We here provide the first evidence that perinatal NP exposure causes myocardial fibrosis in growing male rat pups and reveal the molecular mechanism and functional role of the TGF-β1/LIMK1 signaling pathway in this process.
Fulltext Advanced machine learning schemes for prediction CO2 flux based experimental approach in underground coal fire areas

Wang Y, Guo M, Vo Thanh H, Zhang H, Liu X, Zheng Q, et al.

J Adv Res, 2024 Nov 07.
PMID: 39521430 DOI: 10.1016/j.jare.2024.10.034

INTRODUCTION: Underground coal fires pose significant environmental and health risks due to releasing CO2 emissions. Predicting surface CO2 flux accurately in underground coal fire areas is crucial for understanding the distribution of spontaneous combustion zones and developing effective mitigation strategies. In recent years, advanced machine learning techniques have shown promise in various carbon-related studies. This research uses an experimental approach to explore the power of advanced machine learning schemes for predicting CO2 flux in underground coal fire areas.
OBJECTIVES: By leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO2 flux prediction in coal fire areas and inform environmental monitoring and management strategies.
METHODS: The study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO2 flux and underground coal fire characteristics. Innovative feature engineering techniques are applied to capture the unique characteristics of underground coal fire areas and their impact on CO2 flux. Different machine learning algorithms, including Natural gradient boosting regression (NGRB), Extreme gradient boosting (XGboost), Light gradient boosting (LGRB), and random forest (RF), are evaluated and compared for their predictive capabilities. The models are trained, optimized, and assessed using appropriate performance metrics.
RESULTS: The NGRB model yields the best predictive performances with R2 of 0.967 and MAE of 0.234. The novel contributions of this study include the development of accurate prediction models tailored to underground coal fire areas, shedding light on the underlying factors driving CO2 flux. The findings have practical implications for delineating the spontaneous combustion zone and mitigating CO2 emissions from underground coal fires, contributing to global efforts in combating climate change.
Fulltext The Hippo-YAP signaling pathway drives CD24-mediated immune evasion in esophageal squamous cell carcinoma via macrophage phagocytosis

Zhou X, Yan Z, Hou J, Zhang L, Chen Z, Gao C, et al.

Oncogene, 2024 Feb;43(7):495-510.
PMID: 38168654 DOI: 10.1038/s41388-023-02923-z

Esophageal squamous cell carcinoma (ESCC) is one of the most lethal malignancies in the world with poor prognosis. Despite the promising applications of immunotherapy, the objective response rate is still unsatisfactory. We have previously shown that Hippo/YAP signaling acts as a powerful tumor promoter in ESCC. However, whether Hippo/YAP signaling is involved in tumor immune escape in ESCC remains largely unknown. Here, we show that YAP directly activates transcription of the "don't eat me" signal CD24, and plays a crucial role in driving tumor cells to avoid phagocytosis by macrophages. Mechanistically, YAP regulates CD24 expression by interacting with TEAD and binding the CD24 promoter to initiate transcription, which facilitates tumor cell escape from macrophage-mediated immune attack. Our animal model data and clinical data show that YAP combined with CD24 in tumor microenvironment redefines the impact of TAMs on the prognosis of ESCC patients which will provide a valuable basis for precision medicine. Moreover, treatment with YAP inhibitor altered the distribution of macrophages and suppressed tumorigenesis and progression of ESCC in vivo. Together, our study provides a novel link between Hippo/YAP signaling and macrophage-mediated immune escape, which suggests that the Hippo-YAP-CD24 axis may act as a promising target to improve the prognosis of ESCC patients. A proposed model for the regulatory mechanism of Hippo-YAP-CD24-signaling axis in the tumor-associated macrophages mediated immune escape.
Integrated image-based deep learning and language models for primary diabetes care

Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.

Nat Med, 2024 Jul 19.
PMID: 39030266 DOI: 10.1038/s41591-024-03139-8

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.

Autophagy, 2016;12(1):1-222.
PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
Fulltext Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1

Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.

Autophagy, 2021 Jan;17(1):1-382.
PMID: 33634751 DOI: 10.1080/15548627.2020.1797280

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.