Identification of genetically predicted DNA methylation markers associated with non-small cell lung cancer risk among 34,964 cases and 448,579 controls.

BACKGROUND: Although the associations between genetic variations and lung cancer risk have been explored, the epigenetic consequences of DNA methylation in lung cancer development are largely unknown. Here, the genetically predicted DNA methylation markers associated with non-small cell lung cancer (NSCLC) risk by a two-stage case-control design were investigated. METHODS: The genetic prediction models for methylation levels based on genetic and methylation data of 1595 subjects from the Framingham Heart Study were established. The prediction models were applied to a fixed-effect meta-analysis of screening data sets with 27,120 NSCLC cases and 27,355 controls to identify the methylation markers, which were then replicated in independent data sets with 7844 lung cancer cases and 421,224 controls. Also performed was a multi-omics functional annotation for the identified CpGs by integrating genomics, epigenomics, and transcriptomics and investigation of the potential regulation pathways. RESULTS: Of the 29,894 CpG sites passing the quality control, 39 CpGs associated with NSCLC risk (Bonferroni-corrected p ≤ 1.67 × 10-6 ) were originally identified. Of these, 16 CpGs remained significant in the validation stage (Bonferroni-corrected p ≤ 1.28 × 10-3 ), including four novel CpGs. Multi-omics functional annotation showed nine of 16 CpGs were potentially functional biomarkers for NSCLC risk. Thirty-five genes within a 1-Mb window of 12 CpGs that might be involved in regulatory pathways of NSCLC risk were identified. CONCLUSIONS: Sixteen promising DNA methylation markers associated with NSCLC were identified. Changes of the methylation level at these CpGs might influence the development of NSCLC by regulating the expression of genes nearby. PLAIN LANGUAGE SUMMARY: The epigenetic consequences of DNA methylation in lung cancer development are still largely unknown. This study used summary data of large-scale genome-wide association studies to investigate the associations between genetically predicted levels of methylation biomarkers and non-small cell lung cancer risk at the first time. This study looked at how well larotrectinib worked in adult patients with sarcomas caused by TRK fusion proteins. These findings will provide a unique insight into the epigenetic susceptibility mechanisms of lung cancer.

Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery.

Pervaporation (PV) is an effective membrane separation process for organic dehydration, recovery, and upgrading. However, it is crucial to improve membrane materials beyond the current permeability-selectivity trade-off. In this research, we introduce machine learning (ML) models to identify high-potential polymers, greatly improving the efficiency and reducing cost compared to conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane structure, operating conditions, and solute properties. Dimensionality reduction, missing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.447 and 0.360 for separation factor and total flux, respectively (logarithmic scale). Screening approximately 1 million hypothetical polymers with ML models resulted in identifying polymers with a predicted permeation separation index >30 and synthetic accessibility score <3.7 for acetic acid extraction. This study demonstrates the promise of ML to accelerate tailored membrane designs.

A coating strategy to achieve effective local charge separation for photocatalytic coevolution.

Semiconductors of narrow bandgaps and high quantum efficiency have not been broadly utilized for photocatalytic coevolution of H2 and O2 via water splitting. One prominent issue is to develop effective protection strategies, which not only mitigate photocorrosion in an aqueous environment but also facilitate charge separation. Achieving local charge separation is especially challenging when these reductive and oxidative sites are placed only nanometers apart compared to two macroscopically separated electrodes in a photoelectrochemical cell. Additionally, the driving force of charge separation, namely the energetic difference in the barrier heights across the two type of sites, is small. Herein, we used conformal coatings attached by nanoscale cocatalysts to transform two classes of tunable bandgap semiconductors, i.e., CdS and GaInP2, into stable and efficient photocatalysts. We used hydrogen evolution and redox-mediator oxidation for model study, and further constructed a two-compartment solar fuel generator that separated stoichiometric H2 and O2 products. Distinct from the single charge-transfer direction reported for conventional protective coatings, the coating herein allows for concurrent injection of photoexcited electrons and holes through the coating. The energetic difference between reductive and oxidative catalytic sites was regulated by selectivity and local kinetics. Accordingly, the charge separation behavior was validated using numerical simulations. Following this design principle, the CdS/TiO2/Rh@CrOx photocatalysts evolved H2 while oxidizing reversible polysulfide redox mediators at a maximum rate of 90.6 µmol⋅h-1⋅cm-2 by stacking three panels. Powered by a solar cell, the redox-mediated solar water-splitting reactor regenerated the polysulfide repeatedly and achieved solar-to-hydrogen efficiency of 1.7%.

Highly Selective Electrochemical Nitrate to Ammonia Conversion by Dispersed Ru in a Multielement Alloy Catalyst.

Electrochemical reduction of nitrate to ammonia (NH3) converts an environmental pollutant to a critical nutrient. However, current electrochemical nitrate reduction operations based on monometallic and bimetallic catalysts are limited in NH3 selectivity and catalyst stability, especially in acidic environments. Meanwhile, catalysts with dispersed active sites generally exhibit a higher atomic utilization and distinct activity. Herein, we report a multielement alloy nanoparticle catalyst with dispersed Ru (Ru-MEA) with other synergistic components (Cu, Pd, Pt). Density functional theory elucidated the synergy effect of Ru-MEA than Ru, where a better reactivity (NH3 partial current density of -50.8 mA cm-2) and high NH3 faradaic efficiency (93.5%) is achieved in industrially relevant acidic wastewater. In addition, the Ru-MEA catalyst showed good stability (e.g., 19.0% decay in FENH3 in three hours). This work provides a potential systematic and efficient catalyst discovery process that integrates a data-guided catalyst design and novel catalyst synthesis for a range of applications.

Machine Learning in Environmental Research: Common Pitfalls and Best Practices.

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.

Predicting Extraction Selectivity of Acetic Acid in Pervaporation by Machine Learning Models with Data Leakage Management.

The extraction of acetic acid and other carboxylic acids from water is an emerging separation need as they are increasingly produced from waste organics and CO2 during carbon valorization. However, the traditional experimental approach can be slow and expensive, and machine learning (ML) may provide new insights and guidance in membrane development for organic acid extraction. In this study, we collected extensive literature data and developed the first ML models for predicting separation factors between acetic acid and water in pervaporation with polymers' properties, membrane morphology, fabrication parameters, and operating conditions. Importantly, we assessed seed randomness and data leakage problems during model development, which have been overlooked in ML studies but will result in over-optimistic results and misinterpreted variable importance. With proper data leakage management, we established a robust model and achieved a root-mean-square error of 0.515 using the CatBoost regression model. In addition, the prediction model was interpreted to elucidate the variables' importance, where the mass ratio was the topmost significant variable in predicting separation factors. In addition, polymers' concentration and membranes' effective area contributed to information leakage. These results demonstrate ML models' advances in membrane design and fabrication and the importance of vigorous model validation.

Upscaling 3D Engineered Trees for Off-Grid Desalination.

More than 70% of the population without access to safe drinking water lives in remote and off-grid areas. Inspired by natural plant transpiration, we designed and tested in this study an array of scalable three-dimensional (3D) engineered trees made of natural wood for continuous water desalination to provide affordable and clean drinking water. The trees took advantage of capillary action in the wood xylems and lifted water more than 1 foot off the ground with or without solar irradiation. This process overcame some major challenges of popular solar-driven water evaporation and water harvesting, such as intermittent operation, low water production rate, and system scaling. The trade-off between energy transfer and system footprint was tackled by optimizing the interspacing between the trees. The scaled system has a ratio of surface area (vapor generation) to project area (water transport) up to 118, significantly higher than the prevailing flat-sheet design. The extensive surface area evaporated water at a temperature cooler than the surrounding air, drawing on multiple environmental energy sources including solar, wind, or ambient heat in the air and realized continuous operation. The total energy for evaporation reached over 300% of the one-sun irradiance, enabling a freshwater production rate of 4.8 L m-2 h-1 from an array of 16 trees in an enclosed room and 14 L m-2 h-1 under a 3 m/s airflow. Furthermore, we found that the ambient heat in the air contributed 60%-70% of the total latent heat of vaporization when energy sources were decoupled. During long-term desalination tests, the engineered trees demonstrated a self-cleaning mechanism with daily cycles of salt accumulation and dissolution. Combining the quantification from an evaporation model and meteorology data covering the globe, we also demonstrated that the 3D engineered trees can be of particular interest for sustainable desalination in the Middle East and North Africa (MENA) regions.

Effects of organic matter and dewaterability changes on sludge calorific value during acid treatment.

Acid treatment can increase the sludge calorific value to some extent by separating inorganic elements. In order to determine the mechanism by which acidification affects the sludge calorific value from an organic perspective, we investigated the changes in organic matter and dewaterability under different pH conditions. The results of this study showed that acidification conditioning retained organic matter while removing a greater amount of inorganic elements. Furthermore, acid treatment significantly increased the zeta potential and particle size of sludge particles and facilitated the precipitation of biological organic components from the supernatant to the surface of sludge particles. Acid-treated sludge exhibited a lower moisture content and a higher proportion of organic matter, and sludge treated with H2SO4, HCl, and HNO3 exhibited respective increases in calorific values of 12.14%, 7.92%, and 8.01% under pH 2. The calorific value of the acid-treated sludge was higher, making it more suitable for subsequent incineration. The findings of this study serve as a reference and foundation for efficient sludge incineration.

MICDnet: Multimodal information processing networks for Crohn's disease diagnosis.

Crohn's disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn's disease, medical professionals need to comprehensively analyze patients' multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn's disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn's disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models.

CBLC promotes the development of colorectal cancer by promoting ABI1 degradation to activate the ERK signaling pathway.

CBLC (CBL proto-oncogene C) is an E3 ubiquitin protein ligase that plays a key role in cancers. However, the function and mechanism of CBLC in colorectal cancer (CRC) has not been fully elucidated. The aim of this study was to investigate the function of CBLC in CRC and its underlying molecular mechanism. High CBLC levels were certified in tumor tissues of CRC patients, and its expression was positively associated with TNM stage. Next, we explored the role of CBLC in CRC using gain or loss of function. For biological function analysis, CCK-8 cell proliferation, colony formation, flow cytometry, scratch, and transwell assays collectively suggested that CBLC overexpression promoted cell proliferation, cell cycle progression, migration and invasion. As observed, CBLC knockdown exhibited exactly opposite effects, resulting in impaired tumorigenicity in vitro. Xenograft studies displayed that CBLC overexpression accelerated tumor growth and promoted tumor metastasis to the lung, while the inhibitory effects of CBLC knockdown on tumorigenicity and metastasis ability of CRC cells was also confirmed. Furthermore, the molecular mechanism of CBLC in CRC was explored. CBLC induced the activation of ERK signaling pathway, further leading to its pro-tumor role. Notably, CBLC decreased ABI1 (Abelson interactor protein-1, a candidate tumor suppressor) protein levels through its ubiquitin ligase activity, while ABI1 upregulation abolished the effects of CBLC on the tumorigenesis of CRC. Taken together, these results demonstrate that CBLC acts as a tumor promoter in CRC through triggering the ubiquitination and degradation of ABI1 and activating the ERK signaling pathway. CBLC may be a potential novel target for CRC.

Association between sleep traits and biological aging risk: a Mendelian randomization study based on 157 227 cases and 179 332 controls.

STUDY OBJECTIVES: To investigate whether sleep traits are associated with the risk of biological aging using a case-control design with Mendelian randomization (MR) analyses. METHODS: We studied 336 559 participants in the UK Biobank cohort, including 157 227 cases of accelerated biological aging and 179 332 controls. PhenoAge, derived from clinical traits, estimated biological ages, and the discrepancies from chronological age were defined as age accelerations (PhenoAgeAccel). Sleep behaviors were assessed with a standardized questionnaire. propensity score matching matched control participants to age-accelerated participants, and a conditional multivariable logistic regression model estimated odds ratio (OR) and 95% confidence intervals (95% CI). Causal relationships between sleep traits and PhenoAgeAccel were explored using linear and nonlinear MR methods. RESULTS: A U-shaped association was found between sleep duration and PhenoAgeAccel risk. Short sleepers had a 7% higher risk (OR = 1.07; 95% CI: 1.03 to 1.11), while long sleepers had an 18% higher risk (OR = 1.18; 95% CI: 1.15 to 1.22), compared to normal sleepers (6-8 hours/day). Evening chronotype was linked to higher PhenoAgeAccel risk than morning chronotype (OR = 1.14; 95% CI: 1.10 to 1.18), while no significant associations were found for insomnia or snoring. Morning chronotype had a protective effect on PhenoAgeAccel risk (OR = 0.87, 95% CI: 0.79 to 0.95) per linear MR analysis. Genetically predicted sleep duration showed a U-shaped relationship with PhenoAgeAccel, suggesting a nonlinear association (pnonlinear < 0.001). CONCLUSIONS: The study suggests that improving sleep can slow biological aging, highlighting the importance of optimizing sleep as an intervention to mitigate aging's adverse effects.

DNA methylation marker identification and poly-methylation risk score in prediction of healthspan termination.

Aim: To elucidate the epigenetic consequences of DNA methylation in healthspan termination (HST), considering the current limited understanding. Materials & methods: Genetically predicted DNA methylation models were established (n = 2478). These models were applied to genome-wide association study data on HST. Then, a poly-methylation risk score (PMRS) was established in 241,008 individuals from the UK Biobank. Results: Of the 63,046 CpGs from the prediction models, 13 novel CpGs were associated with HST. Furthermore, people with high PMRSs showed higher HST risk (hazard ratio: 1.18; 95% CI: 1.13-1.25). Conclusion: The study indicates that DNA methylation may influence HST by regulating the expression of genes (e.g., PRMT6, CTSK). PMRSs have a promising application in discriminating subpopulations to facilitate early prevention.

Xiezhuo Tiaozhi formula inhibits macrophage pyroptosis in the non-alcoholic fatty liver disease by targeting the SIRT1 pathway.

BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a challenging disease to interfere with and represents a potential long-term risk factor for hepatic fibrosis and liver cancer. The Xiezhuo Tiaozhi (XZTZ) formula, a water extract from crude herbs, has been widely used as an anti-NAFLD agent through clinical observation. However, the underlying pharmacological mechanisms of the XZTZ formula and its impact on the potential pathways against NAFLD have not been elucidated. PURPOSE: Our study aims to investigate the pharmacological effects and underlying regulatory mechanisms of the XZTZ formula to treat NAFLD. METHODS: The possible active components and pharmacological mechanisms of the XZTZ formula against NAFLD were identified using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) and molecular docking. To further explore the potential mechanisms, forty-eight 6-week-old male C57BL/6 J mice were given individual attention with high-fat and high-sugar diet (HFHSD) or relevant control (Ctrl) diets for 16 weeks to successfully construct a NAFLD mouse model. Subsequently, the levels of serum biochemicals, pathological changes in the liver, and pyroptosis levels were assessed in mice to investigate the therapeutic effects of the XZTZ formula. Further, LPS-induced RAW264.7 cells and Immortalized Mouse Kupffer cells (ImKC) were used to verify the potential mechanisms of the XZTZ formula against NAFLD in vitro. RESULTS: We identified 7 chemical compounds and 2 potential therapeutic targets as plausible therapeutic points for the treatment of NAFLD using the XZTZ formula. Subsequent histopathological analysis revealed marked hepatic steatosis and lipid accumulation in the HFHSD mice liver, while conditions were effectively ameliorated by administration of the XZTZ formula. Additionally, our work demonstrated that the XZTZ formula could attenuate M1 polarization, promote M2 polarization, and suppress pyroptosis via the SIRT1 pathway in tissue samples. Moreover, validation performed through LPS-induced RAW264.7 and ImKC cells by showing that silencing SIRT1 weaken the effects of the XZTZ formula on relative pyroptosis affirmed that its role was associated with the SIRT1 pathway in macrophage. CONCLUSION: These findings suggest that the XZTZ formula alleviated hepatic steatosis and lipid accumulation in NAFLD mice. These ameliorations are associated with mechanisms involving the attenuation of M1 polarization, promotion of M2 polarization, and anti-pyroptosis effects through the SIRT1 pathway.

A singular plasmonic-thermoelectric hollow nanostructure inducing apoptosis and cuproptosis for catalytic cancer therapy.

Thermoelectric technology has recently emerged as a distinct therapeutic modality. However, its therapeutic effectiveness is significantly limited by the restricted temperature gradient within living organisms. In this study, we introduce a high-performance plasmonic-thermoelectric catalytic therapy utilizing urchin-like Cu2-xSe hollow nanospheres (HNSs) with a cascade of plasmonic photothermal and thermoelectric conversion processes. Under irradiation by a 1064 nm laser, the plasmonic absorption of Cu2-xSe HNSs, featuring rich copper vacancies (VCu), leads to a rapid localized temperature gradient due to their exceptionally high photothermal conversion efficiency (67.0%). This temperature gradient activates thermoelectric catalysis, generating toxic reactive oxygen species (ROS) targeted at cancer cells. Density functional theory calculations reveal that this vacancy-enhanced thermoelectric catalytic effect arises from a much more carrier concentration and higher electrical conductivity. Furthermore, the exceptional photothermal performance of Cu2-xSe HNSs enhances their peroxidase-like and catalase-like activities, resulting in increased ROS production and apoptosis induction in cancer cells. Here we show that the accumulation of copper ions within cancer cells triggers cuproptosis through toxic mitochondrial protein aggregation, creating a synergistic therapeutic effect. Tumor-bearing female BALB/c mice are used to evaluate the high anti-cancer efficiency. This innovative approach represents the promising instance of plasmonic-thermoelectric catalytic therapy, employing dual pathways (membrane potential reduction and thioctylated protein aggregation) of mitochondrial dysfunction, all achieved within a singular nanostructure. These findings hold significant promise for inspiring the development of energy-converting nanomedicines.

Piezoelectric Bilayer Nickel-Iron Layered Double Hydroxide Nanosheets with Tumor Microenvironment Responsiveness for Intensive Piezocatalytic Therapy.

Piezocatalytic therapy (PCT) based on 2D layered materials has emerged as a promising non-invasive tumor treatment modality, offering superior advantages. However, a systematic investigation of PCT, particularly the mechanisms underlying the reactive oxygen species (ROS) generation by 2D nanomaterials, is still in its infancy. Here, for the first time, biodegradable piezoelectric 2D bilayer nickel-iron layered double hydroxide (NiFe-LDH) nanosheets (thickness of ≈1.86 nm) are reported for enhanced PCT and ferroptosis. Under ultrasound irradiation, the piezoelectric semiconducting NiFe-LDH exhibits a remarkable ability to generate superoxide anion radicals, due to the formation of a built-in electric field that facilitates the separation of electrons and holes. Notably, the significant excitonic effect in the ultrathin NiFe-LDH system enables long-lived excited triplet excitons (lifetime of ≈5.04 µs) to effectively convert triplet O2 molecules into singlet oxygen. Moreover, NiFe-LDH exhibited tumor microenvironment (TME)-responsive peroxidase (POD)-like and glutathione (GSH)-depleting capabilities, further enhancing oxidative stress in tumor cells and inducing ferroptosis. To the best of knowledge, this is the first report on piezoelectric semiconducting sonosensitizers based on LDHs for PCT and ferroptosis, providing a comprehensive understanding of the piezocatalysis mechanism and valuable references for the application of LDHs and other 2D materials in cancer therapy.

Xiaoke Bitong capsule alleviates inflammatory impairment via inhibition of the TNF signaling pathway to against diabetic peripheral neuropathy.

BACKGROUND: Xiaoke Bitong capsule (XBC) is a crude herbal compound believed to tonify qi, improve blood circulation, and alleviate blood stasis. It has been used as an herbal formula for the prevention and treatment of diabetic peripheral neuropathy (DPN) under the guidance of traditional Chinese medicine (TCM). However, the pharmacological mechanisms by which XBC ameliorates DPN remain poorly understood. The interaction between pro-inflammatory factors and the activation of tumor necrosis factor (TNF) plays a critical role in the underlying mechanisms of DPN. XBC may protect against DPN through the regulation of the TNF pathway. PURPOSE: Many studies show the association between DPN and nerve dysfunction, however, treatment options are limited. To identify specific therapeutic targets and active components of XBC that contribute to its anti-DPN effects, our study aimed to investigate the potential mechanism of action of XBC during the progression of DPN using a system pharmacology approach. METHODS: An approach involving UPLC-Q-TOF/MS and network pharmacology was used to analyze the compositions, potential targets, and active pathways of XBC. Further, models of streptozocin (STZ) induced mouse and glucose induced RSC96 cells were established to explore the therapeutic effects of XBC. High glucose induced RSC96 cells were pretreated with small interfering RNA (siRNA) to identify potential therapeutic targets of DPN. RESULTS: Seventy-one active compositions of XBC and five potential targets, including mitogen-activated protein kinase 8 (MAPK), interleukin-6 (IL-6), poly-ADP-ribose polymerase-1 (PARP1), vascular endothelial growth factor A (VEGFA), and transcription factor p65 (NF-κB), were considered as the potential regulators of DPN. In addition, the results revealed that the TNF signaling pathway was closely related to DPN. Moreover, DPN contributed to the decreased expressions of PI3K and AKT, increased TNF-α and IL-1ß in RSC96 cells, which were both reversed by XBC or TNF-α siRNA. CONCLUSION: XBC could protect against DPN by inhibiting the release of pro-inflammatory cytokines and regulating the activation of the TNF signaling pathway, further accelerating neurogenesis, and alleviating peripheral nerve lesions. Therefore, this study highlights the therapeutic value of XBC for DPN.

Long noncoding RNA PSMA3-AS1 functions as a competing endogenous RNA to promote gastric cancer progression by regulating the miR-329-3p/ALDOA axis.

LncRNA PSMA3-AS1 functions as an oncogene in several cancers, including ovarian cancer, lung cancer, and colorectal cancer. However, its role in gastric cancer (GC) progression remains unclear. In this study, the levels of PSMA3-AS1, miR-329-3p, and aldolase A (ALDOA) in 20 paired human GC tissues and adjacent nontumorous tissues were measured by real-time PCR. GC cells were transfected with recombinant plasmid carrying full-length PSMA3-AS1 or shRNA targeting PSMA3-AS1. The stable transfectants were selected by G418. Then, the effects of PSMA3-AS1 knockdown or overexpression on GC progression in vitro and in vivo were evaluated. The results showed that PSMA3-AS1 was highly expressed in human GC tissues. Stable knockdown of PSMA3-AS1 significantly restrained proliferation/migration/invasion, enhanced cell apoptosis, and induced oxidative stress in vitro. Tumor growth and matrix metalloproteinase expression in tumor tissues were markedly inhibited, while oxidative stress was enhanced in nude mice after stable PSMA3-AS1 knockdown. Additionally, PSMA3-AS1 negatively regulated miR-329-3p while positively regulated ALDOA expression. MiR-329-3p directly targeted ALDOA-3'UTR. Interestingly, miR-329-3p knockdown or ALDOA overexpression partially attenuated the tumor-suppressive effects of PSMA3-AS1 knockdown. Conversely, PSMA3-AS1 overexpression exhibited the opposite effects. PSMA3-AS1 promoted GC progression by regulating the miR-329-3p/ALDOA axis. PSMA3-AS1 might serve as a promising and effective target for GC treatment.

Mucosal esophageal carcinoma following endoscopic submucosal dissection with giant gastric metastasis: A case report and review of literature.

BACKGROUND: Esophageal carcinoma is a highly aggressive digestive cancer responsible for a notable proportion of cancer-related deaths worldwide. Its elevated metastatic rate contributes to a poor prognosis in affected patients. In this case review, we aim to summarize the metastatic characteristics of intramural gastric metastasis (IGM) in mucosal esophageal squamous carcinoma. CASE SUMMARY: A 56-year-old man was admitted to our hospital because of a dry cough with an esophageal sensation for one year. Endoscopic examination revealed a 2.0 cm 1.0 cm, superficial esophageal squamous cell carcinoma, and the patient underwent endoscopic submucosal dissection (ESD). Fifteen months after ESD, positron emission tomography/computed tomography revealed that the metabolism of the stomach cardia wall had increased slightly. However, the mucosa of the gastric cardia was smooth under gastroendoscopy. Two years after ESD, endoscopic examination revealed a giant gastric cardia carcinoma, while the esophageal mucosa was smooth, and no advanced cancer was found. A biopsy of the gastric cardia indicated squamous-cell carcinoma. The patient received immunochemotherapy and radiotherapy for esophageal cancer for 8 mo and is currently under follow-up. CONCLUSION: Early-stage esophageal carcinoma with IGM is rare. Despite the ESD of the primary lesion, IGM may still occur and should be closely monitored after ESD.

Oxygen-Vacancy-Rich Piezoelectric BiO2- x Nanosheets for Augmented Piezocatalytic, Sonothermal, and Enzymatic Therapies.

Piezocatalytic therapy is a new-emerging reactive oxygen species (ROS)-enabled therapeutic strategy that relies on built-in electric field and energy-band bending of piezoelectric materials activated by ultrasound (US) irradiation. Despite becoming a hot topic, material development and mechanism exploration are still underway. Herein, as-synthesized oxygen-vacancy-rich BiO2- x nanosheets (NSs) demonstrate outstanding piezoelectric properties. Under US, a piezo-potential of 0.25 V for BiO2- x NSs is sufficient to tilt the conduction band to be more negative than the redox potentials of O2 /• O2 - , • O2 - /H2 O2 , and H2 O2 /• OH, which initiates a cascade reaction for ROS generation. Moreover, the BiO2- x NSs exhibit peroxidase and oxidase-like activities to augment ROS production, especially in the H2 O2 -overexpressed tumor microenvironment. Density functional theory calculations show that the generated oxygen vacancies in BiO2- x NSs are favorable for H2 O2  adsorption and increasing the carrier density to produce ROS. Furthermore, the quick movement of electrons enables an excellent sonothermal effect, for example, rapid rise in temperature to nearly 65 °C upon US with low power (1.2 W cm-2 ) and short time (96 s). Therefore, this system realizes a multimode synergistic combination of piezocatalytic, enzymatic, and sonothermal therapies, providing a new direction for defect engineering-optimized piezoelectric materials for tumor therapy.