Up to 50-70% of liver cirrhosis patients develop hepatic encephalopathy (HE), which is closely related to gut microbiota dysbiosis, with unclear mechanism. Here, through constructing gut-brain modules to assess bacterial neurotoxins from metagenomic datasets, we found phenylalanine decarboxylase (PDC) genes, mainly from Ruminococcus gnavus (R. gnavus), increased ~10-folds in cirrhosis and higher in HE patients. Cirrhotic, not healthy mice, colonized with R. gnavus showed brain phenylethylamine (PEA) accumulation, along with memory impairment, symmetrical tremors, and cortex-specific neuron loss, typically found in HE patients. This accumulation of PEA was primarily driven by decreased monoamine oxidase-B (MAO-B) activity in both the liver and serum due to cirrhosis. Targeting PDC or PEA reversed the neurological symptoms induced by R. gnavus. Furthermore, fecal microbiota transplantation from HE patients to germ-free cirrhotic mice replicated these symptoms and further corroborated the efficacy of targeting PDC or PEA. Clinically, high baseline PEA levels were linked to a 7-folds increased risk of HE post-intrahepatic portosystemic shunt procedures. Our findings expand the understanding of gut-liver-brain axis and identify a promising therapeutic and predictive target for HE.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

This dataset contains county-level information for U.S. counties from 2020 to 2022, aiming to explore the potential relationship between COVID-19 vaccination coverage and the prevalence of severe depression. It integrates multiple data sources, including public health statistics, socioeconomic indicators, environmental variables, and demographic characteristics. The dataset is structured to support spatial, temporal, and statistical analysis.Key Variables Include:Mental Health: Severe depression rates per 100,000 population for 2021 and 2022COVID-19 Metrics: Case rates per 100,000 (2021, 2022), and vaccination rates (2-dose complete, 5+ population)Socioeconomic Data: Unemployment rates, median household income, percent of adults with bachelor's degree or higherEnvironmental Factors: Average daily sunlight (KJ/m²), cooling degree daysDemographics: Population size, gender distribution, age distribution, urbanization rateHealth Behavior Indicators: Rates of smoking, obesity, physical inactivity, and excessive drinkingLog-transformed versions of several variables are also included to support regression modeling and machine learning tasks.Purpose:
The dataset is curated for research that investigates the interplay between COVID-19 vaccination campaigns and mental health outcomes, with potential applications in spatial epidemiology, public health policy, and social determinants of health research.Temporal Coverage: 2020–2022
Geographic Scope: U.S. counties (N ≈ 3,000+)
Data Format: Xlsx
Suggested Citation: Wencong Cui, Yuqing Wang, "COVID-19 Vaccination and Depression: U.S. County-Level Dataset (2020–2022)", Figshare, 2025. DOI: 10.6084/m9.figshare.29451644

This dataset contains county-level information for U.S. counties from 2020 to 2022, aiming to explore the potential relationship between COVID-19 vaccination coverage and the prevalence of severe depression. It integrates multiple data sources, including public health statistics, socioeconomic indicators, environmental variables, and demographic characteristics. The dataset is structured to support spatial, temporal, and statistical analysis.Key Variables Include:Mental Health: Severe depression rates per 100,000 population for 2021 and 2022COVID-19 Metrics: Case rates per 100,000 (2021, 2022), and vaccination rates (2-dose complete, 5+ population)Socioeconomic Data: Unemployment rates, median household income, percent of adults with bachelor's degree or higherEnvironmental Factors: Average daily sunlight (KJ/m²), cooling degree daysDemographics: Population size, gender distribution, age distribution, urbanization rateHealth Behavior Indicators: Rates of smoking, obesity, physical inactivity, and excessive drinkingLog-transformed versions of several variables are also included to support regression modeling and machine learning tasks.Purpose:
The dataset is curated for research that investigates the interplay between COVID-19 vaccination campaigns and mental health outcomes, with potential applications in spatial epidemiology, public health policy, and social determinants of health research.Temporal Coverage: 2020–2022
Geographic Scope: U.S. counties (N ≈ 3,000+)
Data Format: Xlsx
Suggested Citation: Wencong Cui, Yuqing Wang, "COVID-19 Vaccination and Depression: U.S. County-Level Dataset (2020–2022)", Figshare, 2025. DOI: 10.6084/m9.figshare.29451644

Supplementary information files for article "Pressure-controlled nanopipette sensing in the asymmetric-conductivity configuration"

Nanopipettes are important tools across diverse disciplines, including biology, physics, and materials science. Precisely controlling their characteristics is crucial for many applications. Recent progress in this endeavor has involved using the asymmetric-conductivity configuration with different electrolyte solutions inside and outside the nanopipette, which can greatly improve nanopipette sensing. However, understanding such measurements remains challenging due to the complex interplay of diffusion, electromigration, and electroosmosis. Here, we systematically explore a fundamental regime of the asymmetric-conductivity configuration where classical ion current rectification due to ion-selective migration is minimized and the effect of electroosmotic flow is maximized. We characterized the current-potential and current-distance relationship and revealed that this experimental configuration exhibits many of the characteristics of traditionally rectifying nanopipettes, such as surface charge sensitivity, while the current response can be understood simply from the rate and direction of solution mixing due to electroosmotic flow. To optimize the sensitivity in the asymmetric-conductivity configuration, we introduced a method that uses external pressure to control the fluid flow rates at the aperture, tuning the local ionic environment in situ.
©American Chemical Society, CC BY-NC-ND 4.0

Supplementary information files for article "Pressure-controlled nanopipette sensing in the asymmetric-conductivity configuration"

Nanopipettes are important tools across diverse disciplines, including biology, physics, and materials science. Precisely controlling their characteristics is crucial for many applications. Recent progress in this endeavor has involved using the asymmetric-conductivity configuration with different electrolyte solutions inside and outside the nanopipette, which can greatly improve nanopipette sensing. However, understanding such measurements remains challenging due to the complex interplay of diffusion, electromigration, and electroosmosis. Here, we systematically explore a fundamental regime of the asymmetric-conductivity configuration where classical ion current rectification due to ion-selective migration is minimized and the effect of electroosmotic flow is maximized. We characterized the current-potential and current-distance relationship and revealed that this experimental configuration exhibits many of the characteristics of traditionally rectifying nanopipettes, such as surface charge sensitivity, while the current response can be understood simply from the rate and direction of solution mixing due to electroosmotic flow. To optimize the sensitivity in the asymmetric-conductivity configuration, we introduced a method that uses external pressure to control the fluid flow rates at the aperture, tuning the local ionic environment in situ.
©American Chemical Society, CC BY-NC-ND 4.0

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

A total of 33 subjects were enrolled in this study. Bronchoalveolar lavage (BAL) fluid was collected from all the subjects. Bacterial DNA was then isolation and analyzed by 16S rDNA sequencing.

A total of 33 subjects were enrolled in this study. Bronchoalveolar lavage (BAL) fluid was collected from all the subjects. Bacterial DNA was then isolation and analyzed by 16S rDNA sequencing.