[Phenotypic drug discovery promotes research and development of innovative drugs based on traditional Chinese medicine].

Traditional Chinese medicine with rich resources in China and definite therapeutic effects on complex diseases demonstrates great development potential. However, the complex composition, the unclear pharmacodynamic substances and mechanisms of action, and the lack of reasonable methods for evaluating clinical safety and efficacy have limited the research and development of innovative drugs based on traditional Chinese medicine. The progress in cutting-edge disciplines such as artificial intelligence and biomimetics, especially the emergence of cell painting and organ-on-a-chip, helps to identify and characterize the active ingredients in traditional Chinese medicine based on the changes in model characteristics, thus providing more accurate guidance for the development and application of traditional Chinese medicine. The application of phenotypic drug discovery in the research and development of innovative drugs based on traditional Chinese medicine is gaining increasing attention. In recent years, the technology for phenotypic drug discovery keeps advancing, which improves the early discovery rate of new drugs and the success rate of drug research and development. Accordingly, phenotypic drug discovery gradually becomes a key tool for the research on new drugs. This paper discusses the enormous potential of traditional Chinese medicine in the discovery and development of innovative drugs and illustrates how the application of phenotypic drug discovery, supported by cutting-edge technologies such as cell painting, deep learning, and organ-on-a-chip, propels traditional Chinese medicine into a new stage of development.

One-Step Electrochemically Prepared Bionic Hierarchical Nickel Black@Graphene Composite Membrane for Desalination by Solar-Thermal Energy Conversion.

Ingenious microstructure construction and appropriate composition selection are effective strategies for achieving enhanced performance of photothermal materials. Herein, a broccoli-like hierarchical nickel black@graphene (Ni@Gr) membrane for solar-driven desalination was prepared by a one-step electrochemical method, which was carried out simultaneously with the electrochemical exfoliation of graphene and the co-deposition of Ni@Gr material. The bionic hierarchical structure and the chemical composition of the Ni@Gr membrane increased the sunlight absorption (90.36%) by the light-trapping effect and the introduction of graphene. The Ni@Gr membrane achieved high evaporation rates of 2.05 and 1.16 kg m-2 h-1 under simulated (1 sun) and outdoor sunlight conditions, respectively. The superhydrophilicity and the hierarchical structure of the Ni@Gr membrane jointly reduced the evaporation enthalpy (1343.6 kJ/kg), which was beneficial to break the theoretical limit of the evaporation rate (1.47 kg m-2 h-1). This work encourages the application of bionic metal-carbon composite photothermal materials in solar water evaporation.

Advances in immunotherapy of M2 macrophages and gastrointestinal stromal tumor.

Gastrointestinal stromal tumors (GIST) are the most common mesenchymal-derived tumors of the GI tract. They can occur throughout the GI tract, and the survival time of some patients can be improved by first-line targeted therapy with imatinib. However, there are some limitations with imatinib treatment. Immunotherapy for GIST has attracted much attention in recent years, and as one of the most abundant cells in the GIST microenvironment, M2 macrophages play an important role in disease progression. They have unique anti-inflammatory and pro-tumorigenic effects and are one target for immunotherapy. This review summarizes the connection between different factors and the programmed death receptor-1/programmed death ligand-1 pathway and M2 macrophages to reactivate or enhance anti-tumor immunity and improve imatinib efficacy, and to provide new ideas for GIST immunotherapy.

Genome-wide association studies of body size traits in Tibetan sheep.

BACKGROUND: Elucidating the genetic variation underlying phenotypic diversity will facilitate improving production performance in livestock species. The Tibetan sheep breed in China holds significant historical importance, serving as a fundamental pillar of Qinghai's animal husbandry sector. The Plateau-type Tibetan sheep, comprising 90% of the province's population, are characterized by their tall stature and serve as the primary breed among Tibetan sheep. In contrast, Zhashijia sheep exhibit larger size and superior meat quality. These two species provide an excellent model for elucidating the genetic basis of body size variation. Therefore, this study aims to conduct a comprehensive genome-wide association study on these two Tibetan sheep breeds to identify single nucleotide polymorphism loci and regulatory genes that influence body size traits in Tibetan sheep. RESULT: In this study, the phenotypic traits of body weight, body length, body height, chest circumference, chest depth, chest width, waist angle width, and pipe circumference were evaluated in two Tibetan sheep breeds: Plateau-type sheep and Zhashijia Tibetan sheep. Whole genome sequencing generated 48,215,130 high-quality SNPs for genome-wide association study. Four methods were applied and identified 623 SNPs significantly associated with body size traits. The significantly associated single nucleotide polymorphisms identified in this study are located near or within 111 candidate genes. These genes exhibit enrichment in the cAMP and Rap1 signaling pathways, significantly affecting animal growth, and body size. Specifically, the following genes were associated: ASAP1, CDK6, FRYL, NAV2, PTPRM, GPC6, PTPRG, KANK1, NTRK2 and ADCY8. CONCLUSION: By genome-wide association study, we identified 16 SNPs and 10 candidate genes associated with body size traits in Tibetan sheep, which hold potential for application in genomic selection breeding programs in sheep. Identifying these candidate genes will establish a solid foundation for applying molecular marker-assisted selection in sheep breeding and improve our understanding of body size control in farmed animals.

Fast Metabolic Glycan Labeling for Click-Labeling and Imaging of Sialoglycans in Living Acute Brain Slices from Mice and Human Patients.

Living acute brain slices provide a practical platform for imaging sialylation in human brain pathology. However, the limited lifespan of acute brain slices has impeded the use of metabolic glycan labeling (MGL), which requires long-term incubation of clickable unnatural sugars such as N-azidoacetylmannosamine (ManNAz) to metabolically incorporate azides into sialoglycans. Here, we report a fast variant of MGL (fMGL), in which ManNAz-6-phosphate enables efficient azidosugar incorporation within 12 h by bypassing the bottleneck step in the sialic acid biosynthesis pathway, followed by click-labeling with fluorophores and imaging of sialoglycans in acute brain slices from mice and human patients. In the clinical samples of ganglioglioma, fMGL-based imaging reveals specific upregulation of sialylation in astrocyte-like but not neuron-like tumor cells. In addition, fMGL is integrated with click-expansion microscopy for high-resolution imaging of sialoglycans in brain slices. The fMGL strategy should find broad applications in the tissue imaging of glycans and surgical pathology.

Optimal use of ß-lactams in neonates: machine learning-based clinical decision support system.

BACKGROUND: Accurate prediction of the optimal dose for ß-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections. METHODS: Five ß-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected. The CDSS was constructed by incorporating the drug, patient, dosage, pharmacodynamic, and microbiological factors. The CatBoost ML algorithm was used to build the CDSS. Real-world studies were used to evaluate the CDSS performance. Virtual trials were used to compare the CDSS-optimized doses with guideline-recommended doses. FINDINGS: For a specific drug, by entering the patient characteristics and pharmacodynamic (PD) target (50%/70%/100% fraction of time that the free drug concentration is above the minimal inhibitory concentration [fT > MIC]), the CDSS can determine whether the planned dosing regimen will achieve the PD target and suggest an optimal dose. The prediction accuracy of all five drugs was >80.0% in the real-world validation. Compared with the PopPK model, the overall accuracy, precision, recall, and F1-Score improved by 10.7%, 22.1%, 64.2%, and 43.1%, respectively. Using the CDSS-optimized doses, the average probability of target concentration attainment increased by 58.2% compared to the guideline-recommended doses. INTERPRETATION: An ML-based CDSS was successfully constructed to assist clinicians in selecting optimal ß-lactam antibiotic doses. FUNDING: This work was supported by the National Natural Science Foundation of China; Distinguished Young and Middle-aged Scholar of Shandong University; National Key Research and Development Program of China.

Pretreatment and analysis techniques development of TKIs in biological samples for pharmacokinetic studies and therapeutic drug monitoring.

Tyrosine kinase inhibitors (TKIs) have emerged as the first-line small molecule drugs in many cancer therapies, exerting their effects by impeding aberrant cell growth and proliferation through the modulation of tyrosine kinase-mediated signaling pathways. However, there exists a substantial inter-individual variability in the concentrations of certain TKIs and their metabolites, which may render patients with compromised immune function susceptible to diverse infections despite receiving theoretically efficacious anticancer treatments, alongside other potential side effects or adverse reactions. Therefore, an urgent need exists for an up-to-date review concerning the biological matrices relevant to bioanalysis and the sampling methods, clinical pharmacokinetics, and therapeutic drug monitoring of different TKIs. This paper provides a comprehensive overview of the advancements in pretreatment methods, such as protein precipitation (PPT), liquid-liquid extraction (LLE), solid-phase extraction (SPE), micro-SPE (µ-SPE), magnetic SPE (MSPE), and vortex-assisted dispersive SPE (VA-DSPE) achieved since 2017. It also highlights the latest analysis techniques such as newly developed high performance liquid chromatography (HPLC) and high-resolution mass spectrometry (HRMS) methods, capillary electrophoresis (CE), gas chromatography (GC), supercritical fluid chromatography (SFC) procedures, surface plasmon resonance (SPR) assays as well as novel nanoprobes-based biosensing techniques. In addition, a comparison is made between the advantages and disadvantages of different approaches while presenting critical challenges and prospects in pharmacokinetic studies and therapeutic drug monitoring.

An ensemble framework-based approach for modeling stability of expansive soil slopes: fusion of machine learning algorithms and protection structure disease data.

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope stability evaluation is critical in geological disaster prevention and control. In this study, the type and characteristics of slope protection structure disease were determined through the field investigation of an expansive soil area, and this information is incorporated into the numerical simulations and works to develop prediction models of slope stability. Four base machine learning (ML) methods are used to capture the relationship between protection structure diseases and factor of safety (FOS). Further, with the help of stacked generalization (SG), four ML models are combined, and the final SG model is used to predict the FOS. The results show that ML methods can effectively utilize this information and achieve excellent prediction results. The proposed SG model exhibits superior accuracy and robustness in predicting FOS compared to other ML methods. With FOS as the regression variable, the main feature contributions are slope height (37.05%) > slip distance of retaining wall (25.43%) > expansive force (18.03%) > slope gradient (12.00%); the coupling relationship among features is also captured by the proposed model. It is concluded that the SG method is particularly suitable for slope stability modeling under small sample conditions. Besides, the SG-based model effectively captures the impact of protection structure diseases on slope stability, enhances the interpretability of the ML model, and provides a reference for the maintenance and repair of the protection structure.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

BACKGROUND: Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. OBJECTIVE: The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. RESULTS: High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. CONCLUSION: The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Coumarins and flavones with anti-inflammatory activity isolated from the branches and leaves of Daphne retusa.

In this study, two new (1, 13) and fourteen known (2-12, 14-16) compounds were isolated from the branches and leaves of Daphne retusa. On the basis of chemical evidence and spectral data analysis (UV, ECD NMR, and HR-ESI-MS), the structures of new compounds were elucidated. Furthermore, all compounds have been tested for their inhibitory effects on NO production in LPS-induced RAW 264.7 cells, and compound 3 showed obvious inhibitory effect. Through target screening and molecular docking technology, potential binding targets for compound 3 to exert anti-inflammatory effects have been predicted.