Formulation and pharmacokinetic evaluation of a paclitaxel nanosuspension for intravenous delivery

Paclitaxel is a diterpenoid isolated from Taxus brevifolia. It is effective for various cancers, especially ovarian and breast cancer. Due to its aqueous insolubility, it is administered dissolved in ethanol and Cremophor® EL (BASF, Ludwigshafen, Germany), which can cause serious allergic reactions. In order to eliminate Cremophor EL, paclitaxel was formulated as a nanosuspension by high-pressure homogenization. The nanosuspension was lyophilized to obtain the dry paclitaxel nanoparticles (average size, 214.4 ± 15.03 nm), which enhanced both the physical and chemical stability of paclitaxel nanoparticles. Paclitaxel dissolution was also enhanced by the nanosuspension. Differential scanning calorimetry showed that the crystallinity of paclitaxel was preserved during the high-pressure homogenization process. The pharmacokinetics and tissue distribution of paclitaxel were compared after intravenous administration of paclitaxel nanosuspension and paclitaxel injection. In rat plasma, paclitaxel nanosuspension exhibited a significantly (P < 0.01) reduced area under the concentration curve (AUC)0–∞ (20.343 ± 9.119 μg · h · mL−1 vs 5.196 ± 1.426 μg · h · mL−1), greater clearance (2.050 ± 0.616 L · kg−1 · h−1 vs 0.556 ± 0.190 L · kg−1 · h−1), and shorter elimination half-life (5.646 ± 2.941 vs 3.774 ± 1.352 hours) compared with the paclitaxel solution. In contrast, the paclitaxel nanosuspension resulted in a significantly greater AUC0–∞ in liver, lung, and spleen (all P < 0.01), but not in heart or kidney

TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT

Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate. The advancements in large language models (LLMs) have made it possible to interact with tables using natural language input, bringing this capability closer to reality. In this paper, we present TableGPT, a unified fine-tuned framework that enables LLMs to understand and operate on tables using external functional commands. It introduces the capability to seamlessly interact with tables, enabling a wide range of functionalities such as question answering, data manipulation (e.g., insert, delete, query, and modify operations), data visualization, analysis report generation, and automated prediction. TableGPT aims to provide convenience and accessibility to users by empowering them to effortlessly leverage tabular data. At the core of TableGPT lies the novel concept of global tabular representations, which empowers LLMs to gain a comprehensive understanding of the entire table beyond meta-information. By jointly training LLMs on both table and text modalities, TableGPT achieves a deep understanding of tabular data and the ability to perform complex operations on tables through chain-of-command instructions. Importantly, TableGPT offers the advantage of being a self-contained system rather than relying on external API interfaces. Moreover, it supports efficient data process flow, query rejection (when appropriate) and private deployment, enabling faster domain data fine-tuning and ensuring data privacy, which enhances the framework's adaptability to specific use cases.Comment: Technical Repor

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Mode of action of a novel synthetic auxin herbicide, halauxifen-methyl

Halauxifen‐methyl is a new auxin herbicide developed by Corteva Agriscience (Wilmington, DE, USA). It has been suggested that ABF5 may be the target of halauxifen‐methyl, as AFB5 mutants of Arabidopsis thaliana are resistant to halauxifen‐methyl, which preferentially binds to AFB5. However, the mode of action of halauxifen-methyl has not yet been reported. Therefore, the aim of the present study was to reveal the mode of action of halauxifen-methyl by exploring its influence on indole-3-acetic acid (IAA) homeostasis and the biosynthesis of ethylene and Abscisic Acid (ABA) in Galium aparine. The results showed that halauxifen-methyl could disrupt the homeostasis of IAA and stimulate the overproduction of ethylene and ABA by inducing the overexpression of 1-aminocyclopropane-1-carboxylate synthase (ACS) and 9-cis-epoxycarotenoid dioxygenase (NCED) genes involved in ethylene and ABA biosynthesis, finally leading to senescence and plant death

Numerical Analysis of the Factors Influencing a Vertical U-Tube Ground Heat Exchanger

The development of a three-dimensional, unsteady state model, which couples heat transfer with groundwater seepage for a vertical U-tube ground heat exchanger (GHE) is presented. The influence of underground soil thermal properties, grout materials, inlet water temperature and velocity, and groundwater seepage on heat transfer in the GHE is examined. The results indicate that before the heat in the borehole is saturated, the heat flux in the GHE is directly proportional to the thermal conductivity coefficient of the grout materials. The radius of the thermal effect of the GHE and the recovery rate of the temperature in the soil are also proportional to the thermal diffusion coefficient of the soil. In cooling mode, the increase of the inlet water temperature of the GHE results in enhanced heat transfer. However, this may cause issues with heat buildup. The increase of the inlet water velocity in the GHE enhances heat convection in the tube. The effect of thermal-seepage coupling in groundwater can reduce the accumulated heat, thus resulting in the effective enhancement of the heat transfer in the GHE

Kinetics and mechanism for reduction of Pt(IV) anticancer model compounds by Se-methyl L-selenocysteine. Comparison with L-selenomethionine.

Se-methyl L-selenocysteine (MeSeCys) is one of the major organic selenium compounds acquired from the diet by human beings. It has been shown to have anticancer activity and cancer prevention functions. However, its antioxidant activity, largely related to its biological function, has not been well characterized so far. We here report a stopped-flow kinetic study of the reduction of the Pt(IV) anticancer model compounds trans-[PtX2(CN)4]2− (X = Cl; Br) by MeSeCys in a wide pH range. Overall second-order kinetics is established for the redox reactions, and spectrophotometric titrations indicate a 1:1 reaction stoichiometry. The MeSeCys is oxidized to its selenoxide form, as identified by high-resolution mass spectra. The proposed reaction mechanism involves parallel attack on one of the trans-coordinated halides of the Pt(IV) complexes by the selenium atom of the various MeSeCys protolytic species. Rate constants for the rate determining steps as well as the pKa values of the various protolytic species of MeSeCys have been determined at 25.0 °C and 1.0 M ionic strength. A bridged two-electron transfer mechanism for the rate-determining steps is supported by rapid-scan spectra, activation parameters, and by the much larger reaction rate of [PtBr2(CN)4]2− compared to [PtCl2(CN)4]2−. The experiments indicate that the reduction of [PtX2(CN)4]2− by MeSeCys proceeds via a similar reaction mechanism as L-selenomethionine (SeMet) studied previously. However, there is a large reactivity difference between these two selenium compounds, as a matter of fact the largest one observed so far when compared to other redox systems. It differs between the various protolytic species of MeSeCys and SeMet. The different reactivity of MeSeCys and SeMet in the reduction of various biologically relevant oxidants might account for their disparate efficacies as anticancer agents

Role of Autophagy in the Radiosensitivity of Human Lung Adenocarcinoma A549 Cells

Background and objective Radiotherapy is an important treatment for lung cancer. The poor prognosis of lung cancer is largely caused by the high recurrence rate and metastasis of the tumor. Autophagy, which can be induced by radiotherapy, might be associated with DNA repair. The aim of this study is to investigate whether activating autophagy using rapamycin can enhance the radiosensitivity of lung cancer cells and clarify the association of autophagy with DNA repair. Methods The human adenocarcinoma A549 cell line was selected as the experimental subject. The specimens were divided into three groups: control (N), radiation (R), and Rapamycin and radiation (R+RAPA). The protein levels of γ-H2AX, Rad51, Ku70/Ku80, p62, and LC3 were determined by Western blot. Autophagosome was observed under a transmission electron microscope, and SF was determined by colony formation assay. Results Compared with group R, the activity of autophagy and the protein expression levels of Rad51 and Ku70/80 were remarkably increased in group R+RAPA. Conclusion The radiosensitivity of lung cancer can be promoted by activating autophagy via treatment with Rapamycin, and the process may be associated with DNA repair

Determination of ploidy level and isolation of genes encoding acetyl-CoA carboxylase in Japanese Foxtail (Alopecurus japonicus).

Ploidy level is important in biodiversity studies and in developing strategies for isolating important plant genes. Many herbicide-resistant weed species are polyploids, but our understanding of these polyploid weeds is limited. Japanese foxtail, a noxious agricultural grass weed, has evolved herbicide resistance. However, most studies on this weed have ignored the fact that there are multiple copies of target genes. This may complicate the study of resistance mechanisms. Japanese foxtail was found to be a tetraploid by flow cytometer and chromosome counting, two commonly used methods in the determination of ploidy levels. We found that there are two copies of the gene encoding plastidic acetyl-CoA carboxylase (ACCase) in Japanese foxtail and all the homologous genes are expressed. Additionally, no difference in ploidy levels or ACCase gene copy numbers was observed between an ACCase-inhibiting herbicide-resistant and a herbicide-sensitive population in this study