Dipeptidyl peptidase-4 marks distinct subtypes of human adipose stromal/stem cells with different hepatocyte differentiation and immunoregulatory properties.

BACKGROUND: Human adipose-derived stromal/stem cells (hASCs) play important roles in regenerative medicine and numerous inflammatory diseases. However, their cellular heterogeneity limits the effectiveness of treatment. Understanding the distinct subtypes of hASCs and their phenotypic implications will enable the selection of appropriate subpopulations for targeted approaches in regenerative medicine or inflammatory diseases. METHODS: hASC subtypes expressing dipeptidyl peptidase-4 (DPP4) were identified via fluorescence-activated cell sorting (FACS) analysis. DPP4 expression was knocked down in DPP4+ hASCs via DPP4 siRNA. The capacity for proliferation, hepatocyte differentiation, inflammatory factor secretion and T-cell functionality regulation of hASCs from DPP4-, DPP4+, and control siRNA-treated DPP4+ hASCs and DPP4 siRNA-treated DPP4+ hASCs were assessed. RESULTS: DPP4+ hASCs and control siRNA-treated DPP4+ hASCs presented a lower proliferative capacity but greater hepatocyte differentiation capacity than DPP4- hASCs and DPP4 siRNA-treated DPP4+ hASCs. Both DPP4+ hASCs and DPP4- hASCs secreted high levels of vascular endothelial growth factor-A (VEGF-A), monocyte chemoattractant protein-1 (MCP-1), and interleukin 6 (IL-6), whereas the levels of other factors, including matrix metalloproteinase (MMP)-1, eotaxin-3, fractalkine (FKN, CX3CL1), growth-related oncogene-alpha (GRO-alpha, CXCL1), monokine induced by interferon-gamma (MIG), macrophage inflammatory protein (MIP)-1beta, and macrophage colony-stimulating factor (M-CSF), were significantly greater in the supernatants of DPP4+ hASCs than in those of DPP4- hASCs. Exposure to hASC subtypes and their conditioned media triggered changes in the secreted cytokine profiles of T cells from healthy donors. The percentage of functional T cells that secreted factors such as MIP-1beta and IL-8 increased when these cells were cocultured with DPP4+ hASCs. The percentage of polyfunctional CD8+ T cells that secreted multiple factors, such as IL-17A, tumour necrosis factor alpha (TNF-α) and TNF-ß, decreased when these cells were cocultured with supernatants derived from DPP4+ hASCs. CONCLUSIONS: DPP4 may regulate proliferation, hepatocyte differentiation, inflammatory cytokine secretion and T-cell functionality of hASCs. These data provide a key foundation for understanding the important role of hASC subpopulations in the regulation of T cells, which may be helpful for future immune activation studies and allow them to be customized for clinical application.

Chemical Constituents, Anti-Tumor Mechanisms, and Clinical Application: A Comprehensive Review on Scutellaria barbata.

With the increasing global incidence and mortality rates of cancer, the development of novel anti-tumor drugs has become particularly urgent. Scutellaria barbata D. Don, a perennial herb belonging to the genus Scutellaria in the family Lamiaceae, has aroused extensive attention for its medicinal value in recent years. This article presents an exhaustive review of the flavonoid, diterpene, and other chemical constituents harbored within Scutellaria barbata, delving into the intricate mechanisms by which these compounds orchestrate their anti-tumor effects via diverse biological pathways. Remarkably, these compounds distinguish themselves through their capability to regulate cellular signaling, inhibit cancer cell proliferation, trigger apoptosis, disrupt angiogenesis, and bolster immune responses. These anti-tumor effects are achieved through strategic modulation of pivotal signaling cascades, particularly the PI3K/Akt/mTOR, MAPK, and NFκB pathways. In addition, this article also summarizes the clinical applications of Scutellaria barbata in tumor treatment, especially its potential in alleviating the side effects of radiotherapy and chemotherapy and improving patients' quality of life. In conclusion, this review comprehensively summarizes and analyzes the chemical constituents, anti-tumor mechanisms, and clinical applications of Scutellaria barbata, with the aim of systematically reviewing the existing research results and exploring potential future research directions.

Advances and Challenges in Immune-Modulatory Biomaterials for Wound Healing Applications.

Wound healing progresses through three distinct stages: inflammation, proliferation, and remodeling. Immune regulation is a central component throughout, crucial for orchestrating inflammatory responses, facilitating tissue repair, and restraining scar tissue formation. Elements such as mitochondria, reactive oxygen species (ROS), macrophages, autophagy, ferroptosis, and cytokines collaboratively shape immune regulation in this healing process. Skin wound dressings, recognized for their ability to augment biomaterials' immunomodulatory characteristics via antimicrobial, antioxidative, pro- or anti-inflammatory, and tissue-regenerative capacities, have garnered heightened attention. Notwithstanding, a lack of comprehensive research addressing how these dressings attain immunomodulatory properties and the mechanisms thereof persists. Hence, this paper pioneers a systematic review of biomaterials, emphasizing immune regulation and their underlying immunological mechanisms. It begins by highlighting the importance of immune regulation in wound healing and the peculiarities and obstacles faced in skin injury recovery. This segment explores the impact of wound metabolism, infections, systemic illnesses, and local immobilization on the immune response during healing. Subsequently, the review examines a spectrum of biomaterials utilized in skin wound therapy, including hydrogels, aerogels, electrospun nanofiber membranes, collagen scaffolds, microneedles, sponges, and 3D-printed constructs. It elaborates on the immunomodulatory approaches employed by these materials, focusing on mitochondrial and ROS modulation, autophagic processes, ferroptosis, macrophage modulation, and the influence of cytokines on wound healing. Acknowledging the challenge of antibiotic resistance, the paper also summarizes promising plant-based alternatives for biomaterial integration, including curcumin. In its concluding sections, the review charts recent advancements and prospects in biomaterials that accelerate skin wound healing via immune modulation. This includes exploring mitochondrial transplantation materials, biomaterial morphology optimization, metal ion incorporation, electrostimulation-enabled immune response control, and the benefits of composite materials in immune-regulatory wound dressings. The ultimate objective is to establish a theoretical foundation and guide future investigations in the realm of skin wound healing and related materials science disciplines.

Online Charge-Generation Derivatization by Electrochemical Radical Cations of Thianthrene: Mass Spectrometry Imaging of Estrogens in Biological Tissues.

Estrogens play a significant role in endocrinology and oncology. Although separation methods coupled with mass spectrometry (MS) have emerged as a powerful tool for studying estrogens, imaging the spatial distributions of estrogens is crucial but remains challenging due to its low endogenous concentration and poor ionization efficiency. Charge-generation derivatization, such as N-alkylpyridinium quaternization and S-methyl thioetherification, represents a method wherein neutral molecules involving analytes and derivatization reagents undergo chemical reactions to establish permanent charges directly onto the analytes to improve detection sensitivity. Here, we developed a novel derivatization reagent, thianthrene (TT), which enabled oxidization to radical cations ([TT]•+) using an electrochemical method and completed the online charge-generation derivatization of estrogens on a mass spectrometry imaging platform. In this strategy, [TT]•+ can efficiently and selectively derivatize estrogens via an electrophilic aromatic substitution reaction. Results indicated that derivatization with [TT]•+ can significantly enhance imaging sensitivity (3 orders of magnitude), enabling the visualization of estrogen and its metabolites in ovarian and breast tissues. Furthermore, a higher mass intensity of these estrogens was captured in breast para-cancerous tissues than in cancerous tissues, which might provide estrogens spatial dimension information for further research on the initiation and progression of breast cancer.

Photoresponsive Multirole Nanoweapon Camouflaged by Hybrid Cell Membrane Vesicles for Efficient Antibacterial Therapy of Pseudomonas aeruginosa-Infected Pneumonia and Wound.

Exploring effective antibacterial approaches for targeted treatment of pathogenic bacterial infections with reduced drug resistance is of great significance. Combinational treatment modality that leverages different therapeutic components can improve the overall effectiveness and minimize adverse effects, thus displaying considerable potential against bacterial infections. Herein, red blood cell membrane fuses with macrophage membrane to develop hybrid cell membrane shell, which further camouflages around drug-loaded liposome to fabricate biomimetic liposome (AB@LRM) for precise antibacterial therapy. Specifically, photoactive agent black phosphorus quantum dots (BPQDs) and classical antibiotics amikacin (AM) are loaded in AB@LRM to accurately target the inflammatory sites through the guidance of macrophage membrane and long residence capability of red blood cell membrane, eventually exerting efficacious antibacterial activities. Besides, due to the excellent photothermal and photodynamic properties, BPQDs act as an efficient antibacterial agent when exposed to near-infrared laser irradiation, dramatically increasing the sensitivity of bacteria to antibiotics. Consequently, the synergistic sterilizing effect produced by AB@LRM further restricts bacterial resistance. Upon laser irradiation, AB@LRM shows superior anti-inflammatory and antibacterial properties in models of P. aeruginosa-infected pneumonia and wounds. Hence, this light-activatable antibacterial nanoplatform with good biocompatibility presents great potential to advance the clinical development in the treatment of bacterial infections.

Black Phosphorus Quantum Dot Loaded Bioinspired Nanoplatform Synergized with aPD-L1 for Multimode Cancer Immunotherapy.

Efforts to prolong the blood circulation time and bypass immune clearance play vital roles in improving the therapeutic efficacy of nanoparticles (NPs). Herein, a multifunctional nanoplatform (BPP@RTL) that precisely targets tumor cells is fabricated by encapsulating ultrasmall phototherapeutic agent black phosphorus quantum dot (BPQD), chemotherapeutic drug paclitaxel (PTX), and immunomodulator PolyMetformin (PM) in hybrid membrane-camouflaged liposomes. Specifically, the hybrid cell membrane coating derived from the fusion of cancer cell membrane and red blood cell membrane displays excellent tumor targeting efficiency and long blood circulation property due to the innate features of both membranes. After collaboration with aPD-L1-based immune checkpoint blockade therapy, a boosted immunotherapeutic effect is obtained due to elevated dendritic cell maturation and T cell activation. Significantly, laser-irradiated BPP@RTL combined with aPD-L1 effectively eliminates primary tumors and inhibits lung metastasis in 4T1 breast tumor model, offering a promising treatment plan to develop personalized antitumor strategy.

Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework.

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

Deep learning-assisted mass spectrometry imaging for preliminary screening and pre-classification of psychoactive substances.

Currently, it is of great urgency to develop a rapid pre-classification and screening method for suspected drugs as the constantly springing up of new psychoactive substances. In most researches, psychoactive substances classification approaches depended on the similar chemical structures and pharmacological action with known drugs. Such approaches could not face the complicated circumstance of emerging new psychoactive substances. Herein, mass spectrometry imaging and convolutional neural networks (CNN) were used for preliminary screening and pre-classification of suspected psychoactive substances. Mass spectrometry imaging was performed simultaneously on two brain slices as one was from blank group and another one was from psychoactive substance-induced group. Then, fused neurotransmitter variation mass spectrometry images (Nv-MSIs) reflecting the difference of neurotransmitters between two slices were achieved through two homemade programs. A CNN model was developed to classify the Nv-MSIs. Compared with traditional classification methods, CNN achieved better estimation accuracy and required minimal data preprocessing. Also, the specific region on Nv-MSIs and weight of each neurotransmitter that affected the classification most could be unraveled by CNN. Finally, the method was successfully applied to assist the identification of a new psychoactive substance seized recently. This sample was identified as cannabinoids, which greatly promoted the screening process.

Blood-Brain Barrier-Penetrating and Lesion-Targeting Nanoplatforms Inspired by the Pathophysiological Features for Synergistic Ischemic Stroke Therapy.

Ischemic stroke is a dreadful vascular disorder that poses enormous threats to the public health. Due to its complicated pathophysiological features, current treatment options after ischemic stroke attack remains unsatisfactory. Insufficient drug delivery to ischemic lesions impeded by the blood-brain barrier (BBB) largely limits the therapeutic efficacy of most anti-stroke agents. Herein, inspired by the rapid BBB penetrability of 4T1 tumor cells upon their brain metastasis and natural roles of platelet in targeting injured vasculatures, a bio-derived nanojacket is developed by fusing 4T1 tumor cell membrane with platelet membrane, which further clothes on the surface of paeonol and polymetformin-loaded liposome to obtain biomimetic nanoplatforms (PP@PCL) for ischemic stroke treatment. The designed PP@PCL could remarkably alleviate ischemia-reperfusion injury by efficiently targeting ischemic lesion, preventing neuroinflammation, scavenging excess reactive oxygen species (ROS), reprogramming microglia phenotypes, and promoting angiogenesis due to the synergistic therapeutic mechanisms that anchor the pathophysiological characteristics of ischemic stroke. As a result, PP@PCL exerts desirable therapeutic efficacy in injured PC12 neuronal cells and rat model of ischemic stroke, which significantly attenuates neuronal apoptosis, reduces infarct volume, and recovers neurological functions, bringing new insights into exploiting promising treatment strategies for cerebral ischemic stroke management.

Predicting gene-level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

The Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational framework to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to interleukin (IL)-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified cytokine-specific genes associated with late pSTAT3 time frames and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems. A record of this paper's transparent peer review process is included in the supplemental information.

Chemoimmunotherapeutic Nanogel for Pre- and Postsurgical Treatment of Malignant Melanoma by Reprogramming Tumor-Associated Macrophages.

Surgery is the primary method to treat malignant melanoma; however, the residual microtumors that cannot be resected completely often trigger tumor recurrence, causing tumor-related mortality following melanoma resection. Herein, we developed a feasible strategy based on the combinational chemoimmunotherapy by cross-linking carboxymethyl chitosan (CMCS)-originated polymetformin (PolyMetCMCS) with cystamine to prepare stimuli-responsive nanogel (PMNG) owing to the disulfide bond in cystamine that can be cleaved by the massive glutathione (GSH) in tumor sites. Then, chemotherapeutic agent doxorubicin (DOX) was loaded in PMNG, which was followed by a hyaluronic acid coating to improve the overall biocompatibility and targeting ability of the prepared nanogel (D@HPMNG). Notably, PMNG effectively reshaped the tumor immune microenvironment by reprogramming tumor-associated macrophage phenotypes and recruiting intratumoral CD8+ T cells owing to the inherited immunomodulatory capability of metformin. Consequently, D@HPMNG treatment remarkably suppressed melanoma growth and inhibited its recurrence after surgical resection, proposing a promising solution for overcoming lethal melanoma recurrence.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy.

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

TCR ligand potency differentially impacts PD-1 inhibitory effects on diverse signaling pathways.

Checkpoint blockade revolutionized cancer therapy, but we still lack a quantitative, mechanistic understanding of how inhibitory receptors affect diverse signaling pathways. To address this issue, we developed and applied a fluorescent intracellular live multiplex signal transduction activity reporter (FILMSTAR) system to analyze PD-1-induced suppressive effects. These studies identified pathways triggered solely by TCR or requiring both TCR and CD28 inputs. Using presenting cells differing in PD-L1 and CD80 expression while displaying TCR ligands of distinct potency, we found that PD-1-mediated inhibition primarily targets TCR-linked signals in a manner highly sensitive to peptide ligand quality. These findings help resolve discrepancies in existing data about the site(s) of PD-1 inhibition in T cells while emphasizing the importance of neoantigen potency in controlling the effects of checkpoint therapy.

Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs.

In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field.

Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework.

The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

Extracellular Vesicles-Derived Hybrid Nanoplatforms for Amplified CD47 Blockade-Based Cancer Immunotherapy.

Immunomodulation of tumor-associated macrophages (TAMs) into tumor-inhibiting M1-like phenotype is a promising but challenging strategy. Cleverly, tumor cells overexpress CD47, a "don't eat me" signal that ligates with the signal regulatory protein alpha (SIRPα) on macrophages to escape phagocytosis. Thus, effective re-education of TAMs into the "eat me" type and blocking the CD47-SIRPα signaling play pivotal roles in tumor immunotherapy. Herein, it is reported that hybrid nanovesicles (hEL-RS17) derived from extracellular vesicles of M1 macrophages and decorated with RS17 peptide, an antitumor peptide that specifically binds to CD47 on tumor cells and blocks CD47-SIRPα signaling, can actively target tumor cells and remodel TAM phenotypes. Consequently, more M1-like TAMs infiltrate into tumor tissue to phagocytize more tumor cells due to CD47 blockade. By further co-encapsulating chemotherapeutic agent shikonin, photosensitizer IR820, and immunomodulator polymetformin in hEL-RS17, an enhanced antitumor effect is obtained due to the combinational treatment modality and close synergy among each component. Upon laser irradiation, the designed SPI@hEL-RS17 nanoparticles exert potent antitumor efficacy against both 4T1 breast tumor and B16F10 melanoma models, which not only suppresses primary tumor growth but also inhibits lung metastasis and prevents tumor recurrence, exhibiting great potential in boosting CD47 blockade-based antitumor immunotherapy.

Characterizing temporal variability and repeatability of dose-dependent functional genomics approach for evaluating triclosan toxification.

Dose-dependent functional genomics approach has shown great advantage in identifying the molecular initiating event (MIE) of chemical toxification and yielding point of departure (POD) at genome-wide scale. However, POD variability and repeatability derived from experimental design (settings of dose, replicate number, and exposure time) has not been fully determined. In this work, we evaluated POD profiles perturbed by triclosan (TCS) using dose-dependent functional genomics approach in Saccharomyces cerevisiae at multiple time points (9 h, 24 h and 48 h). The full dataset (total 9 concentrations with 6 replicates per treatment) at 9 h was subsampled 484 times to generate subsets of 4 dose groups (Dose A - Dose D with varied concentration range and spacing) and 5 replicate numbers (2 reps - 6 reps). Firstly, given the accuracy of POD and the experimental cost, the POD profiles from 484 subsampled datasets demonstrated that the Dose C group (space narrow at high concentrations and wide dose range) with three replicates was best choice at both gene and pathway levels. Secondly, the variability of POD was found to be relatively robustness and stability across different experimental designs, but POD was more dependent on the dose range and interval than the number of replicates. Thirdly, MIE of TCS toxification was identified to be the glycerophospholipid metabolism pathway at all-time points, supporting the ability of our approach to accurately recognize MIE of chemical toxification at both short- and long-term exposure. Finally, we identified and validated 13 key mutant strains involved in MIE of TCS toxification, which could serve as biomarkers for TCS exposure. Taken together, our work evaluated the repeatability of dose-dependent functional genomics approach and the variability of POD and MIE of TCS toxification, which will benefit the experimental design for future dose-dependent functional genomics study.

Delving into the molecular initiating event of cadmium toxification via the dose-dependent functional genomics approach in Saccharomyces cerevisiae.

Determining dose-response relationship is essential for comprehensively revealing chemical-caused effects on organisms. However, uncertainty and complexity of gene/protein interactions cause the inability of traditional toxicogenomic methods (e.g., transcriptomics, proteomics and metabolomics) to effectively establish the direct relationship between chemical exposure and genes. In this work, we built an effective dose-dependent yeast functional genomics approach, which can clearly identify the direct gene-chemical link in the process of cadmium (Cd) toxification from a genome-wide scale with wide range concentrations (0.83, 2.49, 7.48, 22.45, 67.34, 202.03 and 606.1 µM). Firstly, we identified 220 responsive strains, and found that 142, 110, 91, 34, 8, 0 and 0 responsive strains can be respectively modulated by seven different Cd exposure concentrations ranging from high to low. Secondly, our results demonstrated that these genes induced by the high Cd exposure were mainly enriched in the process of cell autophagy, but ones caused by the low Cd exposure were primarily involved in oxidative stress. Thirdly, we found that the top-ranked GO biological processes with the lowest point of departure (POD) were transmembrane transporter complex and mitochondrial respiratory chain complex III, suggesting that mitochondrion might be the toxicity target of Cd. Similarly, nucleotide excision repair was ranked first in KEGG pathway with the least POD, indicating that this dose-dependent functional genomics approach can effectively detect the molecular initiating event (MIE) of cadmium toxification. Fourthly, we identified four key mutant strains (RIP1, QCR8, CYT1 and QCR2) as biomarkers for Cd exposure. Finally, the dose-dependent functional genomics approach also performed well in identifying MIE for additional genotoxicity chemical 4-nitroquinoline-1-oxide (4-NQO) data. Overall, our study developed a dose-dependent functional genomics approach, which is powerful to delve into the MIE of chemical toxification and is beneficial for guiding further chemical risk assessment.

An online derivatization strategy targeting carbon-carbon double bonds by laser-ablation carbon fiber ionization mass spectrometry imaging: Unraveling the spatial characteristic in mountain-cultivated ginseng and garden-cultivated ginseng with different ages.

Serving as a world-renowned tonic, ginseng contains various types of bioactive metabolites. The comprehensive profiling of these metabolites may help explore the nutritional value of ginseng. Due to high variety in chemical structures, simultaneous monitoring of these metabolites remains a challenge. Herein, a high-throughput and high-selectivity online derivatization mass spectrometry imaging strategy targeting CC was developed. As a widely existed chemical group, CC acts like a bridge connecting different kinds of metabolites. [d0]/[d10]-Bis(pyridine) iodine tetrafluoroboride reagent was chosen for the derivatization of CC, the detection sensitivity of which increased about 3 magnitudes after derivatization. Assisted by laser ablation carbon fiber ionization mass spectrometry, the spatial distribution of bioactive metabolites in mountain-cultivated and garden-cultivated ginseng were visualized. The correlation heatmap results revealed that metabolites in mountain-cultivated ginseng hold higher correlation than those in garden-cultivated ginseng. The proposed method showed potential in providing comprehensive information on the nutrient content of foods.

Harnessing nanobiotechnology for cerebral ischemic stroke management.

Cerebral ischemic stroke remains one of the most serious neurological disorders that pose threats to human health, causing a large amount of long-term disability or even death throughout the world. Based on its physiologic and pathological features, there are limited available therapeutic options for effective ischemic stroke management. Encouragingly, a rapid advancement of nanobiotechnology is bringing new insights into exploring more alternative strategies against cerebral ischemic stroke, which can cleverly overcome the limitations related to conventional treatment methods. Therefore, this review focuses on the recent achievements of nanobiotechnology for ischemic stroke management, which emphasizes diverse targeted delivery strategies using various nanoplatforms including liposomes, micelles, polymeric nanoparticles, nanogels, inorganic nanomaterials, and cell-derived nano-vectors based on the pathophysiological features of ischemic stroke. Moreover, different therapeutic approaches against ischemic stroke such as neuroprotection, anti-inflammation, thrombolysis, increased blood-brain barrier penetration and reactive oxygen species scavenging are highlighted. Meanwhile, this review discusses how these versatile nanoplatforms were designed to assist in the treatment of ischemic stroke. Based on this, challenges, opportunities, and future perspectives using nanobiotechnology through rational design for effective ischemic stroke management are revealed.