Investigation of the Driving Force of Replacing Adsorbed Hydrocarbons by CO2 in Organic Matter from an Energy Perspective.

Carbon dioxide (CO2) has been widely used to enhance the recovery of adsorbed hydrocarbons from the organic matter (OM) in shale formations. To reveal the driving force of replacing adsorbed hydrocarbons from OM by CO2, we performed molecular dynamics (MD) simulations of the replacement process and calculated the interaction forces between CO2 and hydrocarbons. In addition, based on the umbrella sampling method, steered MD simulations were performed, and the free energy profiles of hydrocarbons were obtained using the weighted histogram analysis method. Results show that the condition of the hydrocarbon replacement by CO2 requires the hydrocarbon to have sufficient kinetic energy or to have a sufficiently large attractive force exerted to ensure that the hydrocarbon escapes the potential well of the OM. The attractive forces exerted on hydrocarbon molecules by CO2 can significantly decrease the energy barrier associated with hydrocarbon movement away from the OM surface. Furthermore, both CO2 and supercritical CO2 can effectively displace adsorbed hydrocarbon gas (methane) on the OM, while supercritical CO2 is required to enhance the recovery of adsorbed hydrocarbon oil (n-dodecane). The results obtained in this study provide guidance for enhancing the recovery of adsorbed hydrocarbons by CO2 in shale formations.

Rational construction of a high-quality and high-efficiency biosynthetic system and fermentation optimization for A82846B based on combinatorial strategies in Amycolatopsis orientalis.

BACKGROUND: Oritavancin is a new generation of semi-synthetic glycopeptide antibiotics against Gram-positive bacteria, which served as the first and only antibiotic with a single-dose therapeutic regimen to treat ABSSSI. A naturally occurring glycopeptide A82846B is the direct precursor of oritavancin. However, its application has been hampered by low yields and homologous impurities. This study established a multi-step combinatorial strategy to rationally construct a high-quality and high-efficiency biosynthesis system for A82846B and systematically optimize its fermentation process to break through the bottleneck of microbial fermentation production. RESULTS: Firstly, based on the genome sequencing and analysis, we deleted putative competitive pathways and constructed a better A82846B-producing strain with a cleaner metabolic background, increasing A82846B production from 92 to 174 mg/L. Subsequently, the PhiC31 integrase system was introduced based on the CRISPR-Cas12a system. Then, the fermentation level of A82846B was improved to 226 mg/L by over-expressing the pathway-specific regulator StrR via the constructed PhiC31 system. Furthermore, overexpressing glycosyl-synthesis gene evaE enhanced the production to 332 mg/L due to the great conversion of the intermediate to target product. Finally, the scale-up production of A82846B reached 725 mg/L in a 15 L fermenter under fermentation optimization, which is the highest reported yield of A82846B without the generation of homologous impurities. CONCLUSION: Under approaches including blocking competitive pathways, inserting site-specific recombination system, overexpressing regulator, overexpressing glycosyl-synthesis gene and optimizing fermentation process, a multi-step combinatorial strategy for the high-level production of A82846B was developed, constructing a high-producing strain AO-6. The combinatorial strategies employed here can be widely applied to improve the fermentation level of other microbial secondary metabolites, providing a reference for constructing an efficient microbial cell factory for high-value natural products.

Identification of a human type XVII collagen fragment with high capacity for maintaining skin health.

Collagen XVII (COL17) is a transmembrane protein that mediates skin homeostasis. Due to expression of full length collagen was hard to achieve in microorganisms, arising the needs for selection of collagen fragments with desired functions for microbial biosynthesis. Here, COL17 fragments (27-33 amino acids) were extracted and replicated 16 times for recombinant expression in Escherichia coli. Five variants were soluble expressed, with the highest yield of 223 mg/L. The fusion tag was removed for biochemical and biophysical characterization. Circular dichroism results suggested one variant (sample-1707) with a triple-helix structure at >37 °C. Sample-1707 can assemble into nanofiber (width, 5.6 nm) and form hydrogel at 3 mg/mL. Sample-1707 was shown to induce blood clotting and promote osteoblast differentiation. Furthermore, sample-1707 exhibited high capacity to induce mouse hair follicle stem cells differentiation and osteoblast migration, demonstrating a high capacity to induce skin cell regeneration and promote wound healing. A strong hydrogel was prepared from a chitosan and sample-1707 complex with a swelling rate of >30 % higher than simply using chitosan. Fed-batch fermentation of sample-1707 with a 5-L bioreactor obtained a yield of 600 mg/L. These results support the large-scale production of sample-1707 as a biomaterial for use in the skin care industry.

Celastrol reduces lung inflammation induced by multiwalled carbon nanotubes in mice via NF-κb-signaling pathway.

Multiwalled carbon nanotubes (MWCNTs) have numerous applications in the field of carbon nanomaterials. However, the associated toxicity concerns have increased significantly because of their widespread use. The inhalation of MWCNTs can lead to nanoparticle deposition in the lung tissue, causing inflammation and health risks. In this study, celastrol, a natural plant medicine with potent anti-inflammatory properties, effectively reduced the number of inflammatory cells, including white blood cells, neutrophils, and lymphocytes, and levels of inflammatory cytokines, such as IL-1ß, IL-6, and TNF-α, in mice lungs exposed to MWCNTs. Moreover, celastrol inhibited the activation of the NF-κB-signaling pathway. This study confirmed these findings by demonstrating comparable reductions in inflammation upon exposure to MWCNTs in mice with the deletion of NF-κB (P50-/-). These results indicate the utility of celastrol as a promising pharmacological agent for preventing MWCNT-induced lung tissue inflammation.

Epitope screening and self-assembled nanovaccine molecule design of PDCoV-S protein based on immunoinformatics.

Based on the whole virus or spike protein of pigs, δ coronavirus (PDCoV) as an immunogen may have unrelated antigenic epitope interference. Therefore, it is essential for screening and identifying advantageous protective antigen epitopes. In addition, immunoinformatic tools are described as an important aid in determining protective antigenic epitopes. In this study, the primary, secondary, and tertiary structures of vaccines were measured using ExPASy, PSIPRED 4.0, and trRosetta servers. Meanwhile, the molecular docking analysis and vector of the candidate nanovaccine were constructed. The immune response of the candidate vaccine was simulated and predicted using the C-ImmSim server. This experiment screened B cell epitopes with strong immunogenicity and high conservation, CTL epitopes, and Th epitopes with IFN-γ and IL-4 positive spike proteins. Ferritin is used as a self-assembled nanoparticle element for designing candidate nanovaccine. After analysis, it has been found to be soluble, stable, non-allergenic, and has a high affinity for its target receptor, TLR-3. The preliminary simulation analysis results show that the candidate nanovaccine has the ability to induce a humoral and cellular immune response. Therefore, it may provide a new theoretical basis for research on coronavirus self-assembled nanovaccines. It may be an effective candidate vaccine for controlling and preventing PDCoV.

Polystyrene Nanoplastics Induce Lipid Metabolism Disorder by Activating the PERK-ATF4 Signaling Pathway in Mice.

In recent years, the study of microplastics (MPs) and nanoplastics (NPs) and their effects on human health has gained significant attention. The impacts of NPs on lipid metabolism and the specific mechanisms involved remain poorly understood. To address this, we utilized high-throughput sequencing and molecular biology techniques to investigate how endoplasmic reticulum (ER) stress might affect hepatic lipid metabolism in the presence of polystyrene nanoplastics (PS-NPs). Our findings suggest that PS-NPs activate the PERK-ATF4 signaling pathway, which in turn upregulates the expression of genes related to lipid synthesis via the ATF4-PPARγ/SREBP-1 pathway. This activation leads to an abnormal accumulation of lipid droplets in the liver. 4-PBA, a known ER stress inhibitor, was found to mitigate the PS-NPs-induced lipid metabolism disorder. These results demonstrate the hepatotoxic effects of PS-NPs and clarify the mechanisms of abnormal lipid metabolism induced by PS-NPs.

Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles.

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.

Butylparaben induces glycolipid metabolic disorders in mice via disruption of gut microbiota and FXR signaling.

Butylparaben, a common preservative, is widely used in food, pharmaceuticals and personal care products. Epidemiological studies have revealed the close relationship between butylparaben and diabetes; however the mechanisms of action remain unclear. In this study, we administered butylparaben orally to mice and observed that exposure to butylparaben induced glucose intolerance and hyperlipidemia. RNA sequencing results demonstrated that the enrichment of differentially expressed genes was associated with lipid metabolism, bile acid metabolism, and inflammatory response. Western blot results further validated that butylparaben promoted hepatic lipogenesis, inflammation, gluconeogenesis, and insulin resistance through the inhibition of the farnesoid X receptor (FXR) pathway. The FXR agonists alleviated the butylparaben-induced metabolic disorders. Moreover, 16 S rRNA sequencing showed that butylparaben reduced the abundance of Bacteroidetes, S24-7, Lactobacillus, and Streptococcus, and elevated the Firmicutes/Bacteroidetes ratio. The gut microbiota dysbiosis caused by butylparaben led to decreased bile acids (BAs) production and increased inflammatory response, which further induced hepatic glycolipid metabolic disorders. Our results also demonstrated that probiotics attenuated butylparaben-induced disturbances of the gut microbiota and hepatic metabolism. Taken collectively, the findings reveal that butylparaben induced gut microbiota dysbiosis and decreased BAs production, which further inhibited FXR signaling, ultimately contributing to glycolipid metabolic disorders in the liver.

Review of Detection Limits for Various Techniques for Bacterial Detection in Food Samples.

Foodborne illnesses can be infectious and dangerous, and most of them are caused by bacteria. Some common food-related bacteria species exist widely in nature and pose a serious threat to both humans and animals; they can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacterial detection are of paramount importance in food safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical methods have been widely used in food safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical methods for various bacterial species (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)), considering different food sample types, analytical performances and the reported limit of detection (LOD), are discussed. It was found that the bacteria species and food sample type contributed significantly to the analytical performance and LOD. Detection via LFIA has a higher average LOD (24 CFU/mL) than detection via electrochemical methods (12 CFU/mL) and PCR (6 CFU/mL). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. Gold and iron nanoparticles were the most studied in the reported articles for LFIA, and average LODs were 26 CFU/mL and 12 CFU/mL, respectively. The electrochemical method revealed that the average LOD was highest for cyclic voltammetry (CV) at 18 CFU/mL, followed by electrochemical impedance spectroscopy (EIS) at 12 CFU/mL and differential pulse voltammetry (DPV) at 8 CFU/mL. LOD usually decreases when the sample number increases until it remains unchanged. Exponential relations (R2 > 0.95) between LODs of Listeria in milk via LFIA and via the electrochemical method with sample numbers have been obtained. Finally, the review discusses challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve analytical performance for bacterial detection.

Mulberry Leaf Compounds and Gut Microbiota in Alzheimer's Disease and Diabetes: A Study Using Network Pharmacology, Molecular Dynamics Simulation, and Cellular Assays.

Recently, studies have reported a correlation that individuals with diabetes show an increased risk of developing Alzheimer's disease (AD). Mulberry leaves, serving as both a traditional medicinal herb and a food source, exhibit significant hypoglycemic and antioxidative properties. The flavonoid compounds in mulberry leaf offer therapeutic effects for relieving diabetic symptoms and providing neuroprotection. However, the mechanisms of this effect have not been fully elucidated. This investigation aimed to investigate the combined effects of specific mulberry leaf flavonoids (kaempferol, quercetin, rhamnocitrin, tetramethoxyluteolin, and norartocarpetin) on both type 2 diabetes mellitus (T2DM) and AD. Additionally, the role of the gut microbiota in these two diseases' treatment was studied. Using network pharmacology, we investigated the potential mechanisms of flavonoids in mulberry leaves, combined with gut microbiota, in combating AD and T2DM. In addition, we identified protein tyrosine phosphatase 1B (PTP1B) as a key target for kaempferol in these two diseases. Molecular docking and molecular dynamics simulations showed that kaempferol has the potential to inhibit PTP1B for indirect treatment of AD, which was proven by measuring the IC50 of kaempferol (279.23 µM). The cell experiment also confirmed the dose-dependent effect of kaempferol on the phosphorylation of total cellular protein in HepG2 cells. This research supports the concept of food-medicine homology and broadens the range of medical treatments for diabetes and AD, highlighting the prospect of integrating traditional herbal remedies with modern medical research.

LEVA: Using Large Language Models to Enhance Visual Analytics.

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

Deubiquitylase USP7 plays a crucial role in the lineage differentiation of preimplantation blastocysts.

Preimplantation embryos undergo a series of important biological events, including epigenetic reprogramming and lineage differentiation, and the key genes and specific mechanisms that regulate these events are critical to reproductive success. USP7 is a deubiquitinase involved in the regulation of a variety of cellular functions, yet its precise function and mechanism in preimplantation embryonic development remain unknown. Our results showed that RNAi-mediated silencing of USP7 in mouse embryos or treatment with P5091, a small molecule inhibitor of USP7, significantly reduced blastocyst rate and blastocyst quality, and decreased total and TE cell numbers per blastocyst, as well as destroying normal lineage differentiation. The results of single-cell RNA-seq, RT-qPCR, western blot, and immunofluorescence staining indicated that interference with USP7 caused failure of the morula-to-blastocyst transition and was accompanied by abnormal expression of key genes (Cdx2, Oct4, Nanog, Sox2) for lineage differentiation, decreased transcript levels, increased global DNA methylation, elevated repressive histone marks (H3K27me3), and decreased active histone marks (H3K4me3 and H3K27ac). Notably, USP7 may regulate the transition from the morula to blastocyst by stabilizing the target protein YAP through the ubiquitin-proteasome pathway. In conclusion, our results suggest that USP7 may play a crucial role in preimplantation embryonic development by regulating lineage differentiation and key epigenetic modifications.

Discovery of novel and selective farnesoid X receptor antagonists through structure-based virtual screening, preliminary structure-activity relationship study, and biological evaluation.

Farnesoid X receptor (FXR) is a bile acids receptor and plays a crucial role in regulating bile acids, lipids, and glucose metabolism. Previous research suggests that inhibiting FXR activation can be beneficial in reducing cholesterol and low-density lipoprotein cholesterol (LDL-C) levels, offering potential treatment options for metabolic syndrome with lipid disorders. Herein, we report p-acetylaminobenzene sulfonate derivatives as a novel scaffold of FXR antagonists by multistage screening. Among these derivatives, compound F44-A13 exhibited a half-maximal inhibitory concentration of 1.1 µM. Furthermore, compound F44-A13 demonstrated effective inhibition of FXR activation in cellular assays and exhibited high selectivity over eleven other nuclear receptors. Besides, compound F44-A13 significantly suppressed the regulation of FXR target genes Shp, Besp, and Cyp7a1, while reducing cholesterol levels in human hepatoma HepG2 cells. Pharmacological studies conducted on C57BL/6 mice further confirmed that compound F44-A13 had beneficial effects in reducing cholesterol, triglycerides, and LDL-C levels. These findings highlight that F44-A13 is a highly selective FXR antagonist that might serve as a useful molecule for further FXR studies as well as the development of FXR antagonists for the potential treatment of metabolic diseases with lipid disorders.

Potential therapeutic strategy for cancer: Multi-dimensional cross-talk between circRNAs and parental genes.

In many ways, circular RNAs (circRNAs) have been demonstrated to be crucial in the onset and advancement of cancer throughout the last ten years and have become a new focus of intense research in the field of RNAs. Accumulating studies have demonstrated that circRNAs can regulate parental gene expression via a variety of biological pathways. Furthermore, research into the complex interactions between circRNAs and their parental genes will shed light on their biological roles and open up new avenues for circRNAs' potential clinical translational uses. However, to date, multi-dimensional cross-talk between circRNAs and parental genes have not been systematically elucidated. Particularly intriguing is circRNA's exploration of tumor targeting, and potential therapeutic uses based on the parental gene regulation perspective. Here, we discuss their biogenesis, take a fresh look at the molecular mechanisms through which circRNAs control the expression of their parental genes in cancer. We further highlight We further highlight the latest circRNA clinical translational applications, including prognostic diagnostic markers, cancer vaccines, gDNA, and so on. Demonstrating the potential benefits and future applications of circRNA therapy.

Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling.

Semantic segmentation of 3D point clouds is important for many applications, such as autonomous driving. To train semantic segmentation models, labeled point cloud segmentation datasets are essential. Meanwhile, point cloud labeling is time-consuming for annotators, which typically involves tuning the camera viewpoint and selecting points with a lasso tool. To reduce the time cost of point cloud labeling, we propose a viewpoint recommendation approach to reduce annotators' labeling time costs. We adapt Fitts' law to model the time cost of lasso selection in point clouds. Using the modeled time cost, the viewpoint that minimizes the lasso selection time cost is recommended to the annotator. We build a data labeling system for semantic segmentation of 3D point clouds that integrates our viewpoint recommendation approach. The system enables users to navigate to recommended viewpoints for efficient annotation. Through a user study, we observed that our approach effectively reduced the data labeling time cost. We also qualitatively compare our approach with previous viewpoint selection approaches on different datasets.

Exposure to early-life adversity and long-term trajectories of multimorbidity among older adults in China: analysis of longitudinal data from the China Health and Retirement Longitudinal Study.

OBJECTIVES: This study aimed to identify long-term distinct trajectories of multimorbidity with ageing from 50 to 85 years among Chinese older adults and examine the relationship between exposure to early-life adversity (ELA; including specific types of adversity and accumulation of different adversities) and these long-term multimorbidity trajectories. DESIGN: The group-based trajectory models identified long-term multimorbidity trajectories. Multinomial logistic regression models were used to examine the relationship between ELA and the identified multimorbidity trajectories. SETTING: This study used data from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018) and the 2014 Life History Survey. PARTICIPANTS: We used data from 9112 respondents (aged 60 and above) of the 2018 wave of CHARLS. OUTCOME MEASURES: Each respondent's history of chronic conditions and experiences of ELA were collected from the 2011-2018 waves of CHARLS and the 2014 Life History Survey. RESULTS: Four heterogeneous long-term trajectories of multimorbidity development were identified: 'maintaining-low' (19.1%), 'low onset-rapidly increasing' (23.3%), 'middle onset-moderately increasing' (41.5%) and 'chronically-high' (16.2%). Our findings indicated that the heterogeneity can be explained by ELA experiences. Across various types of different ELA experiences, exposure to food insufficiency (relative risk ratios from 1.372 (95% CI 1.190 to 1.582) to 1.780 (95% CI 1.472 to 2.152)) and parental quarrel/divorce (relative risk ratios from 1.181 (95% CI 1.000 to 1.394) to 1.262 (95% CI 1.038 to 1.536)) had the most prominent associations with health deterioration. The accumulation of more different ELA experiences was associated with a higher relative risk of developing more severe multimorbidity trajectories (relative risk ratio for five to seven ELAs and chronically high trajectory: 7.555, 95% CI 4.993 to 11.431). CONCLUSIONS: There are heterogeneous long-term trajectories of multimorbidity in Chinese older adults, and the risk of multimorbidity associated with ELA accumulates over the lifespan. Our findings highlight the role of a supportive early-life family environment in promoting health development across the lifespan, advocating for the integration of life-course approaches to implementing health disparity interventions.

Platelet factor 4 promotes deep venous thrombosis by regulating the formation of neutrophil extracellular traps.

The presence of neutrophil extracellular traps (NETs) in thrombotic diseases has been extensively studied. The exact mechanism of NET formation in deep venous thrombosis (DVT) has not been largely studied. This study is aimed to explore the role of NETs and their interaction with platelet factor 4 (PF4) in DVT. In plasma samples from 51 healthy volunteers and 52 DVT patients, NET markers and PF4 were measured using enzyme-linked immunosorbent assays (ELISA). NET generation in blood samples from healthy subjects and DVT patients was analyzed by confocal microscopy and flow cytometry. The plasma levels of NETs were significantly elevated in DVT patients, and neutrophils from patients showed a stronger ability to generate NETs after treatment. PF4 was upregulated in plasma samples from DVT patients and mediated NET formation. NETs enhanced procoagulant (PCA) via tissue factor and activating platelets to induce procoagulant activity. In addition, we established an inferior vena cava ligation (IVC) model to examine the role of NETs in thrombogenicity in DVT. In conclusion, NET formation was mediated by PF4 and enhance the procoagulant activity in DVT.

Nanobubble-actuated ultrasound neuromodulation for selectively shaping behavior in mice.

Ultrasound is an acoustic wave which can noninvasively penetrate the skull to deep brain regions, enabling neuromodulation. However, conventional ultrasound's spatial resolution is diffraction-limited and low-precision. Here, we report acoustic nanobubble-mediated ultrasound stimulation capable of localizing ultrasound's effects to only the desired brain region in male mice. By varying the delivery site of nanobubbles, ultrasound could activate specific regions of the mouse motor cortex, evoking EMG signaling and limb movement, and could also, separately, activate one of two nearby deep brain regions to elicit distinct behaviors (freezing or rotation). Sonicated neurons displayed reversible, low-latency calcium responses and increased c-Fos expression in the sub-millimeter-scale region with nanobubbles present. Ultrasound stimulation of the relevant region also modified depression-like behavior in a mouse model. We also provide evidence of a role for mechanosensitive ion channels. Altogether, our treatment scheme allows spatially-targetable, repeatable and temporally-precise activation of deep brain circuits for neuromodulation without needing genetic modification.

Late-stage modification of bioactive compounds: Improving druggability through efficient molecular editing.

Synthetic chemistry plays an indispensable role in drug discovery, contributing to hit compounds identification, lead compounds optimization, candidate drugs preparation, and so on. As Nobel Prize laureate James Black emphasized, "the most fruitful basis for the discovery of a new drug is to start with an old drug"1. Late-stage modification or functionalization of drugs, natural products and bioactive compounds have garnered significant interest due to its ability to introduce diverse elements into bioactive compounds promptly. Such modifications alter the chemical space and physiochemical properties of these compounds, ultimately influencing their potency and druggability. To enrich a toolbox of chemical modification methods for drug discovery, this review focuses on the incorporation of halogen, oxygen, and nitrogen-the ubiquitous elements in pharmacophore components of the marketed drugs-through late-stage modification in recent two decades, and discusses the state and challenges faced in these fields. We also emphasize that increasing cooperation between chemists and pharmacists may be conducive to the rapid discovery of new activities of the functionalized molecules. Ultimately, we hope this review would serve as a valuable resource, facilitating the application of late-stage modification in the construction of novel molecules and inspiring innovative concepts for designing and building new drugs.