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Tumor vascular endothelial cells are essential constituents of the tumor microenvironment, responsible for delivering oxygen and nutrients that are vital for tumor growth and proliferation. As a hallmark of cancer progression, abnormal tumor vasculature contributes to tumor development through multiple mechanisms. Although anti-angiogenic therapies are widely used in the treatment of various cancers, the intrinsic heterogeneity of endothelial cells poses significant challenges regarding therapeutic efficacy. Therefore, further investigation into the heterogeneity of tumor endothelial cells is of paramount importance. The rapid advancement of single-cell sequencing technologies in recent years has facilitated the detailed characterization of heterogeneity among tumor endothelial cells at the single-cell level, thereby fostering a more precise understanding of the functional roles of individual cells within the tumor microenvironment. This technology has become an indispensable tool for investigating the heterogeneity of tumor endothelial cells, offering insights that could inform the refinement of future cancer treatments. In this review, we synthesize findings from the field of single-cell omics to elucidate the heterogeneous characteristics of tumor endothelial cells. We analyze recent advancements in single-cell technology used in the study of tumor cell heterogeneity in terms of both commonalities and distinctive features, covering aspects at the gene and cellular levels. In this review, we provide an overview of recent applications of single-cell sequencing technology in analyzing tumor endothelial cell heterogeneity, offering insights into the development of precise tumor therapies.

Background During brain aging, disturbances in neuronal phospholipid metabolism result in impaired cognitive function and dysregulation of neurological processes. Mutations in iPLA2β are associated with neurodegenerative conditions that significantly impact brain phospholipids. iPLA2β deficiency exacerbates mitochondrial dysfunction and abnormal mitochondrial accumulation. We hypothesized that iPLA2β contributes to age-related cognitive decline by disrupting neuronal mitophagy. Methodology We used aged wild-type (WT) mice and iPLA2β −/− mice as natural aging models to assess cognitive performance, iPLA2β expression in the cortex, levels of chemokines and inflammatory cytokines, and mitochondrial dysfunction, with a specific focus on mitophagy and the mitochondrial phospholipid profile. To further elucidate the role of iPLA2β, we employed adeno-associated virus (AAV)-mediated iPLA2β overexpression in aged mice and re-evaluated these parameters. Results Our findings revealed a significant reduction in iPLA2β levels in the prefrontal cortex of aged brains. Notably, iPLA2β-deficient mice exhibited impaired learning and memory. Loss of iPLA2β in the PFC of aged mice led to increased levels of chemokines and inflammatory cytokines. This damage was associated with altered mitochondrial morphology, reduced ATP levels due to dysregulation of the parkin-independent mitophagy pathway, and changes in the mitochondrial phospholipid profile. AAV-mediated overexpression of iPLA2β alleviated age-related parkin-independent mitophagy pathway dysregulation in primary neurons and the PFC of aged mice, reduced inflammation, and improved cognitive function. Conclusions Our study suggests that age-related iPLA2β loss in the PFC leads to cognitive decline through the disruption of mitophagy. These findings highlight the potential of targeting iPLA2β to ameliorate age-related neurocognitive disorders.

Background According to the Chinese Pharmacopoeia , the fruit of Schisandra chinensis (Turcz.) Baill. (SC) is an important traditional Chinese medicine that can be used to treat diarrhea. Despite the increasing research on the anti-inflammatory and anti-oxidant aspects of SC, the studies on the anti-ulcerative colitis of Schisandrin (SCH), the main constituent of SC, are relatively few. Methods The mice used in the study were randomly distributed into 6 groups: control, model, 5-ASA, and SCH (20, 40, 80 mg/kg/d). The mice in the model group were administered 3% (w/v) dextran sulfate sodium (DSS) through drinking water for 7 days, and the various parameters of disease activity index (DAI) such as body weight loss, stool consistency, and gross blood were measured. ELISA was used to detect inflammatory factors, and bioinformatics combined with transcriptome analysis was done to screen and verify relevant targets. 16S rDNA high-throughput sequencing was used to analyze the composition of the gut microbiota(GM), while mass spectrometry was done to analyze the changes in the content of bile acids (BAs) in the intestine. Results Mice treated with SCH experienced significant weight gain, effectively alleviating the severity of colitis, and decreasing the levels of inflammatory factors such as TNF-α, IL-1β, IL-18, IL-6, and other related proteins (NLRP3, Caspase-1, SGK1) in UC mice. Furthermore, the analysis of GM and BAs in mice revealed that SCH increased the relative abundance of Lactobacilli spp , reduced the relative abundance of Bacteroides , and promoted the conversion of primary BAs to secondary BAs. These effects contributed to a significant improvement in the DSS-induced GM imbalance and the maintenance of intestinal homeostasis. Conclusion It seems that there is a close relationship between the SCH mechanism and the regulation of SGK1/NLRP3 pathway and the restoration of GM balance. Therefore, it can be concluded that SCH could be a potential drug for the treatment of UC.

Cilia are remarkable cellular devices that power cell motility and transduce extracellular signals. To assemble a cilium, a cylindrical array of 9 doublet microtubules push out an extension of the plasma membrane. Membrane tension regulates cilium formation; however, molecular pathways that link mechanical stimuli to ciliogenesis are unclear. Using genome editing, we introduced hereditary elliptocytosis (HE)- and spinocerebellar ataxia (SCA)-associated mutations into the Caenorhabditis elegans membrane skeletal protein spectrin. We show that these mutations impair mechanical support for the plasma membrane and change cell shape. RNA sequencing (RNA-seq) analyses of spectrin-mutant animals uncovered a global down-regulation of ciliary gene expression, prompting us to investigate whether spectrin participates in ciliogenesis. Spectrin mutations affect intraflagellar transport (IFT), disrupt axonemal microtubules, and inhibit cilium formation, and the endogenous spectrin periodically distributes along cilia. Mammalian spectrin also localizes in cilia and regulates ciliogenesis. These results define a previously unrecognized yet conserved role of spectrin-based mechanical support for cilium biogenesis.

Background: Propylthiouracil (PTU) treats hyperthyroidism and thyroid crisis in all age groups. A variety of serious adverse effects can occur during clinical use and require attention to its pharmacokinetic and pharmacodynamic characteristics in various populations. Objective: To provide information for individualized dosing and clinical evaluation of PTU in the clinical setting by developing a physiologically based pharmacokinetic (PBPK) model, predicting ADME characteristics, and extrapolating to elderly and pediatric populations. Methods: Relevant databases and literature were retrieved to collect PTU’s pharmacochemical properties and ADME parameters, etc. A PBPK model for adults was developed using PK-Sim® software to predict tissue distribution and extrapolated to elderly and pediatric populations. The mean fold error (MFE) method was used to compare the differences between predicted and observed values to assess the accuracy of the PBPK model. The model was validated using PTU pharmacokinetic data in healthy adult populations. Result: The MFE ratios of predicted to observed values of AUC 0-t , C max , and T max were mainly within 0.5 and 2. PTU concentrations in various tissues are lower than venous plasma concentrations. Compared to healthy adults, the pediatric population requires quantitative adjustment to the appropriate dose to achieve the same plasma exposure levels, while the elderly do not require dose adjustments. Conclusion: The PBPK model of PTU was successfully developed, externally validated, and applied to tissue distribution prediction and special population extrapolation, which provides a reference for clinical individualized drug administration and evaluation.

Levetiracetam (LEV) is an anti‐epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically‐based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole‐body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh‐A (CP‐A), Child Pugh‐B (CP‐B), and Child Pugh‐C (CP‐C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Warfarin is a widely used anticoagulant, and its S‐enantiomer has higher potency compared to the R‐enantiomer. S‐warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug–drug interactions (DDIs). This study provides a whole‐body physiologically‐based pharmacokinetic/PD (PBPK/PD) model of S‐warfarin for predicting the effects of drug–drug−gene interactions on S‐warfarin PKs and PDs. The PBPK/PD model of S‐warfarin was developed in PK‐Sim and MoBi. Drug‐dependent parameters were obtained from the literature or optimized. Of the 34 S‐warfarin plasma concentration‐time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S‐warfarin plasma concentration‐time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration‐time curve to the last measurable timepoint (AUClast) ratio within twofold of the observed values. The anticoagulant effect of S‐warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose‐adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

In order to solve the problem that the traditional operation and maintenance system of photovoltaic operation and power station is mainly based on statistical analysis and multisite and multilevel deployment mode, which wastes software and hardware resources, is not easy to expand and has extremely low efficiency, which cannot meet the urgent needs of users to reduce costs and increase efficiency. This research proposes a methodology for integrating big data, cloud computing, and photovoltaic operation and maintenance. This method constructs the business model, data model, application model, and technical model of the photovoltaic power station operation and maintenance cloud platform. The results obtained are as follows: the system provides diagnostic services for the application system of a 30 MWp photovoltaic power station in a certain place, and a total of 87 defects are found, the defect elimination rate is 88.51%, and the monthly power generation of the power station is increased by 122,529 kWh; the use effect of the system in this research after it goes online. The evaluation is 93.04 points, ranging from very satisfactory (A) to satisfactory (B) and biased towards A, indicating that the use effect is good. It is proved that the successful research and promotion of the system in this research will be of great significance to improve the intelligent operation and maintenance level of photovoltaic power plants and improve the operation and maintenance efficiency of photovoltaic power plants.

BackgroundDigoxin is a commonly used cardiac glycoside drug in clinical practice, primarily transported by P-glycoprotein (P-gp) and susceptible to the influence of P-gp inhibitors/inducers. Concurrent administration of ritonavir and digoxin may significantly increase the plasma concentration of digoxin. Due to the narrow therapeutic window of digoxin, combined use may lead to severe toxic effects.PurposeUtilize a Physiology-Based Pharmacokinetic (PBPK) model to simulate and predict the impact of the interaction between ritonavir and digoxin on the pharmacokinetics (PK) of digoxin, and provide recommendations for the combined medication regimen.MethodsUsing PK-Sim®, develop individual PBPK models for ritonavir and digoxin. Simulate the exposure in a drug-drug interaction (DDI) scenario by implementing ritonavir's inhibition of P-glycoprotein (P-gp) on digoxin. Evaluate the performance of the models by comparing the predicted and observed plasma concentration–time curves and predicted versus observed PK parameter values. Finally, adjust the dosing regimen for the combined therapy based on the changes in exposure.ResultsAccording to the model simulations, the steady-state exposure of digoxin increased by 86.5% and 90.2% for oral administration, and 80.2% and 90.2% for intravenous administration, respectively, when 0.25 mg or 0.5 mg of digoxin was administered concurrently with ritonavir. By reducing the dose of digoxin by 45% or doubling the oral administration interval, similar steady-state concentrations can be achieved compared to when the drugs are not co-administered.ConclusionsIn clinical practice, the influence of drug interactions on the plasma concentration changes of digoxin within the body should be considered to ensure the safety and effectiveness of treatment.

BackgroundVenlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.PurposeA physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).MethodsThe parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model’s performance was evaluated by comparing predicted and observed values of plasma concentration–time (PCT) curves and PK parameters values.ResultsIn the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.ConclusionsIn clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.