Pharmacokinetics, tissue distribution, and plasma protein binding ratio of bicuculline following intragastric and intravenous administration in rats using ultra-high-performance liquid chromatography-tandem mass spectrometry.

Bicuculline is a natural isoquinoline alkaloid that works as a gamma-aminobutyric acid receptor antagonist. It is widely found in Papaveraceae plants used in traditional Chinese medicines. Bicuculline not only has been shown to have favorable analgesic, memory-improving, and anxiolytic effects but may also cause adverse effects such as convulsions and epilepsy. A simple, rapid, and sensitive method was developed and validated for the determination of bicuculline in the plasma and tissue samples in rats by ultra-high-performance liquid chromatography-tandem mass spectrometry (MS/MS). The chromatographic separation was performed on a Thermo Scientific C18 column. The MS/MS system was operated in the positive multiple reaction monitoring mode, and the precursor-product ion transitions were optimized as m/z 368.0 → 307.1 for bicuculline and as 354.1 → 188.1 for protopine (internal standard). The linearity, accuracy, precision, recovery, and matrix effect were within acceptable limits. The experimental data showed that bicuculline was rapidly absorbed and eliminated in rats, with a moderate plasma protein binding ratio and low bioavailability. The main tissues of distribution were the kidney, liver, and brain; bicuculline could exert its pharmacological effects across the blood-brain barrier. This study has positive implications for the clinical use of herbal medicines containing bicuculline and for further development.

Fertilizer application alters cadmium and selenium bioavailability in soil-rice system with high geological background levels.

The co-occurrence of cadmium (Cd) pollution and selenium (Se) deficiency commonly exists in global soils, especially in China. As a result, there is great interest in developing practical agronomic strategies to simultaneously achieve Cd remediation and Se mobilization in paddy soils, thereby enhancing food quality/safety. To this end, we conducted a field-plot trial on soils having high geological background levels of Cd (0.67 mg kg-1) and Se (0.50 mg kg-1). We explored 12 contrasting fertilizers (urea, potassium sulfate (K2SO4), calcium-magnesium-phosphate (CMP)), amendments (manure and biochar) and their combinations on Cd/Se bioavailability. Soil pH, total organic carbon (TOC), soil available Cd/Se, Cd/Se fractions and Cd/Se accumulation in different rice components were determined. No significant differences existed in mean grain yield among treatments. Results showed that application of urea and K2SO4 decreased soil pH, whereas the CMP fertilizer and biochar treatments increased soil pH. There were no significant changes in TOC concentrations. Three treatments (CMP, manure, biochar) significantly decreased soil available Cd, whereas no treatment affected soil available Se at the maturity stage. Four treatments (CMP, manure, biochar and manure＋urea＋CMP＋K2SO4) achieved our dual goal of Cd reduction and Se enrichment in rice grain. Structural equation modeling (SEM) demonstrated that soil available Cd and root Cd were negatively affected by pH and organic matter (OM), whereas soil available Se was positively affected by pH. Moreover, redundancy analysis (RDA) showed strong positive correlations between soil available Cd, exchangeable Cd and reducible Cd with grain Cd concentration, as well as between pH and soil available Se with grain Se concentration. Further, there was a strong negative correlation between residual Cd/Se (non-available fraction) and grain Cd/Se concentrations. Overall, this study identified the primary factors affecting Cd/Se bioavailability, thereby providing new guidance for achieving safe production of Se-enriched rice through fertilizer/amendment management of Cd-enriched soils.

Unpaired virtual histological staining using prior-guided generative adversarial networks.

Fibrosis is an inevitable stage in the development of chronic liver disease and has an irreplaceable role in characterizing the degree of progression of chronic liver disease. Histopathological diagnosis is the gold standard for the interpretation of fibrosis parameters. Conventional hematoxylin-eosin (H&E) staining can only reflect the gross structure of the tissue and the distribution of hepatocytes, while Masson trichrome can highlight specific types of collagen fiber structure, thus providing the necessary structural information for fibrosis scoring. However, the expensive costs of time, economy, and patient specimens as well as the non-uniform preparation and staining process make the conversion of existing H&E staining into virtual Masson trichrome staining a solution for fibrosis evaluation. Existing translation approaches fail to extract fiber features accurately enough, and the decoder of staining is unable to converge due to the inconsistent color of physical staining. In this work, we propose a prior-guided generative adversarial network, based on unpaired data for effective Masson trichrome stained image generation from the corresponding H&E stained image. Conducted on a small training set, our method takes full advantage of prior knowledge to set up better constraints on both the encoder and the decoder. Experiments indicate the superior performance of our method that surpasses the previous approaches. For various liver diseases, our results demonstrate a high correlation between the staging of real and virtual stains (ρ=0.82; 95% CI: 0.73-0.89). In addition, our finetuning strategy is able to standardize the staining color and release the memory and computational burden, which can be employed in clinical assessment.

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

BACKGROUND: Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy. METHODS: We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (H&E) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also applied the model to identify whether there was tumor or not in a region, as well as distinguishing squamous cell carcinoma, adenocarcinoma, and neuroendocrine tumor. FINDINGS: In the within-cohort, our model achieved a top1-accuracy of 91.35% and a top3-accuracy of 97.75%. In the external cohort, our model displayed a good generalizability with a top3-accuracy of 97.44%. The top1 consistency between the results of the model and the immunohistochemistry markers was 83.90% and the top3 consistency was 94.33%. The model obtained an accuracy of 98.98% to identify whether there was tumor or not and an accuracy of 93.85% to differentiate three types of cancers. INTERPRETATION: Our model demonstrated good performance to predict the OBMC from routine histology and had great potential for assisting pathologists with determining the OBMC accurately. FUNDING: National Science Foundation of China (61875102 and 61975089), Natural Science Foundation of Guangdong province (2021A15-15012379 and 2022A1515 012550), Science and Technology Research Program of Shenzhen City (JCYJ20200109110606054 and WDZC20200821141349001), and Tsinghua University Spring Breeze Fund (2020Z99CFZ023).

Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.

Electronic properties of double-atom catalysts for electrocatalytic oxygen evolution reaction in alkaline solution: a DFT study.

In alkaline solution, the electrocatalytic oxygen evolution reaction (OER) of dual transition metal atom (2TM) nitrogen-decorated graphene as a double-atom catalyst (DAC) has received special attention. Here, using density functional theory (DFT) calculations, the OER electrocatalysis of 2TM-pyridine/amino-nitrogen-decorated graphene (2TM-NPAG and 2TM-NPG. 2TM represents FeCo, FeNi, Conti) is studied. The electrocatalytic OER mechanism is that 2TM-NPG acts as the pre-catalyst, while the real catalysts are 2TM-NPAG and 2TM-NPG-O. In particular, CoNi-NPAG and CoNi-NPG-O exhibit higher OER activity compared to state-of-the-art RuO2 at pH = 14. It is confirmed that the potential-determining step is also the rate-determining step. Amino-nitrogen is the main accepter of electrons from CoNi atoms and pyridine-nitrogen is the main acceptor of electrons from nearby C atoms. The role of different N coordination continues to influence the entire electrocatalytic OER process of CoNi-NG. Simultaneously, the overpotential of CoNi-NG is in a volcano-shaped relationship with the electronic properties (oxidation state or d-band center) of the catalytic site of Co. Moreover, CoNi-NPAG and CoNi-NPG-O are the closest to the center of the OER overpotential (a function of the d-band center and oxidation state) contour plot, implying that they exhibit the best catalytic activity among all the CoNi-NG materials. The optimal electronic properties of CoNi-NPAG and CoNi-NPG-O contribute towards their excellent OER performance, and provide a new breakthrough in developing high-performance DACs.