Prediction of arsenic adsorption onto metal organic frameworks and adsorption mechanisms interpretation by machine learning.

Metal-organic frameworks (MOFs) are promising adsorbents for the removal of arsenic (As) from wastewater. The As removal efficiency is influenced by several factors, such as the textural properties of MOFs, adsorption conditions, and As species. Examining all of the relevant factors through traditional experiments is challenging. To predict the As adsorption capacities of MOFs toward organic, inorganic, and total As and reveal the adsorption mechanisms, four machine learning-based models were developed, with the adsorption conditions, MOF properties, and characteristics of different As species as inputs. The results demonstrated that the extreme gradient boosting (XGBoost) model exhibited the best predictive performance (test R2 = 0.93-0.96). The validation experiments demonstrated the high accuracy of the inorganic As-based XGBoost model. The feature importance analysis showed that the concentration of As, the surface area of MOFs, and the pH of the solution were the three key factors governing inorganic-As adsorption, while those governing organic-As adsorption were the concentration of As, the pHpzc value of MOFs, and the oxidation state of the metal clusters. The formation of coordination complexes between As and MOFs is possibly the major adsorption mechanism for both inorganic and organic As. However, electrostatic interaction may have a greater effect on organic-As adsorption than on inorganic-As adsorption. Overall, this study provides a new strategy for evaluating As adsorption on MOFs and discovering the underlying decisive factors and adsorption mechanisms, thereby facilitating the investigation of As wastewater treatment.

Are Published Cancer Care Trial Protocols With Traditional Chinese Medicine Interventions Concordant With SPIRIT-TCM Extension 2018? A Scoping Review on Published Trial Protocols Between 2019 and 2022.

BACKGROUND: The SPIRIT-TCM Extension 2018 was created to guide the design and reporting of Traditional Chinese Medicine (TCM) clinical trial protocols. This study aims to investigate the extent of concordance with this guideline in the relevant field of cancer care research. METHODS: A scoping review of TCM cancer trial protocols published in English and Chinese since January 2019 was conducted. Five major academic databases (MEDLINE, EMBASE, CINAHL, CENTRAL, and China National Knowledge Infrastructure) were searched. Concordance with the SPIRIT-TCM Extension 2018 was assessed by descriptive analysis. RESULTS: Fifty-three TCM cancer care trial protocols were identified, comprising 23 acupuncture, 26 Chinese herbal medicine (CHM), and 4 Tai Chi/Qigong (TCQ) interventions. The majority of the checklist items had a low rate of concordance, especially in the reporting of quality control and safety, dosage, TCM diagnostic patterns, possible interactions between Western Medicine and TCM interventions, and TCM-related outcome assessments. CONCLUSIONS: Although the SPIRIT-TCM Extension 2018 guideline was established through extensive Delphi consultation, there are low rates of concordance between published TCM cancer care clinical trial protocols with the guideline. Further research is necessary to understand the low rate of concordance and how scientific rigors of reporting can be improved in TCM cancer care research.

Prediction of acute inhalation toxicity using in vitro lung surfactant inhibition.

Private consumers and professionals may experience acute inhalation toxicity after inhaling aerosolized impregnation products. The distinction between toxic and non-toxic products is difficult to make for producers and product users alike, as there is no clearly described relationship between the chemical composition of the products and induction of toxicity. The currently accepted method for determination of acute inhalation toxicity is based on experiments on animals; it is time-consuming, expensive and causes stress for the animals. Impregnation products are present on the market in large numbers and amounts and exhibit great variety. Therefore, an alternative method to screen for acute inhalation toxicity is needed. The aim of our study was to determine if inhibition of lung surfactant by impregnation products in vitro could accurately predict toxicity in vivo in mice. We tested 21 impregnation products using the constant flow through set-up of the constrained drop surfactometer to determine if the products inhibited surfactant function or not. The same products were tested in a mouse inhalation bioassay to determine their toxicity in vivo. The sensitivity was 100%, i.e., the in vitro method predicted all the products that were toxic for mice to inhale. The specificity of the in vitro test was 63%, i.e., the in vitro method found three false positives in the 21 tested products. Six of the products had been involved in accidental human inhalation where they caused acute inhalation toxicity. All of these six products inhibited lung surfactant function in vitro and were toxic to mice.