Chloroform is a carcinogen in rodents and its carcinogenicity is secondary to events associated with cytotoxicity and regenerative cell proliferation. In this study, a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model that links the processes of chloroform metabolism, reparable cell damage, cell death, and regenerative cellular proliferation was developed to support a new cancer dose-response assessment for chloroform. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) analysis in a two-step approach: (1) metabolism parameters for male and female mice and rats were estimated against available closed chamber gas uptake data; and (2) PD parameters for each of the four rodent groups were estimated from hepatic and renal labeling index data following inhalation exposures. Subsequently, the resulting rodent PD parameters together with literature values for human age-dependent physiological and metabolism parameters were used to scale up the rodent model to a human model. The human model was used to predict exposure conditions under which chloroform-mediated cytolethality is expected to occur in liver and kidney of adults and children. Using the human model, inhalation Reference Concentrations (RfCs) and oral Reference Doses (RfDs) were derived using an uncertainty factor of 10. Based on liver and kidney dose metrics, the respective RfCs were 0.9 and 0.09 ppm; and the respective RfDs were 0.4 and 3 mg/kg/day.
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http://dx.doi.org/10.1111/j.1539-6924.2007.00987.x | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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December 2025
Department of Biosciences and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.
MicroRNAs (miRNAs) have acquired an increased recognition to unravel the complex molecular mechanisms underlying Diminished Ovarian Reserve (DOR), one of the main responsible for infertility. To investigate the impact of miRNA profiles in granulosa cells and follicular fluid, crucial players in follicle development, this study employed a computational network theory approach to reconstruct potential pathways regulated by miRNAs in granulosa cells and follicular fluid of women suffering from DOR. Available data from published research were collected to create the FGC_MiRNome_MC, a representation of miRNA target genes and their interactions.
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January 2025
Department of Neurology, Jinshan Hospital, Fudan University, 201508 Shanghai, China.
Background: Neuronal cholesterol deficiency may contribute to the synaptopathy observed in Alzheimer's disease (AD). However, the underlying mechanisms remain poorly understood. Intact synaptic vesicle (SV) mobility is crucial for normal synaptic function, whereas disrupted SV mobility can trigger the synaptopathy associated with AD.
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January 2025
Department of Clinical Medicine and Surgery, University of Naples "Federico II", 80131 Naples, Italy.
Background: Thyroid Hormones (THs) critically impact human cancer. Although endowed with both tumor-promoting and inhibiting effects in different cancer types, excess of THs has been linked to enhanced tumor growth and progression. Breast cancer depends on the interaction between bulk tumor cells and the surrounding microenvironment in which mesenchymal stem cells (MSCs) exert powerful pro-tumorigenic activities.
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January 2025
Institute of Translational Medicine, Shanghai University, 200444 Shanghai, China.
Background: Dexamethasone has proven life-saving in severe acute respiratory syndrome (SARS) and COVID-19 cases. However, its systemic administration is accompanied by serious side effects. Inhalation delivery of dexamethasone (Dex) faces challenges such as low lung deposition, brief residence in the respiratory tract, and the pulmonary mucus barrier, limiting its clinical use.
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