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.

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.

Single-cell RNA sequencing for traumatic spinal cord injury.

Traumatic spinal cord injury (tSCI) is a severe injury of the central nervous system (CNS) with complicated pathological microenvironment that results in hemorrhage, inflammation, and scar formation. The microenvironment of the injured spinal cord comprises heterogeneous neurons, glial cells, inflammatory cells, and stroma-related cells. Increasing evidence has indicated that the altered cellular and molecular microenvironment following tSCI is a key factor impeding functional recovery. Single-cell RNA sequencing (scRNA-seq) has provided deep insights into the dynamic cellular and molecular changes in the microenvironment by comprehensively characterizing the diversity of spinal cord cell types. Specifically, scRNA-seq enables the exploration of the molecular mechanisms underlying tSCI by elucidating intercellular communication in spinal cord samples between normal and injury conditions at a single-cell resolution. Here, we first described the pathological and physiological processes after tSCI and gave a brief introduction of the scRNA-seq technology. We then focused on the recent scRNA-seq researches in tSCI, which characterized diverse cell-type populations and specific cell-cell interactions in tSCI. In addition, we also highlighted some potential directions for the research of scRNA-seq in tSCI in the future.

Single-cell sequencing reveals microglia induced angiogenesis by specific subsets of endothelial cells following spinal cord injury.

Spinal cord injury (SCI) results in dynamic alterations of the microenvironment at the lesion site, which inevitably leads to neuronal degeneration and functional impairment. The destruction of the spinal vascular system leads to a significant deterioration of the milieu, which exacerbates inflammatory response and deprives cells of nutrient support in the lesion. Limited endogenous angiogenesis occurs after SCI, but the cellular events at the lesion site during this process are unclear so far. Here, we performed single-cell RNA sequencing (scRNA-seq) on spinal cord tissues of rats at different time points after SCI. After clustering and cell-type identification, we focused on vascular endothelial cells (ECs), which play a pivotal role in angiogenesis, and drew the cellular and molecular atlas for angiogenesis after SCI. We found that microglia and macrophages promote endogenous angiogenesis by regulating EC subsets through SPP1 and IGF signaling pathways. Our results indicate that immune cells promote angiogenesis by regulating specific subsets of vascular ECs, which provides new clues for exploring SCI intervention.

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.

Online Quaternized Derivatization Mapping and Glycerides Profiling of Cancer Tissues by Laser Ablation Carbon Fiber Ionization Mass Spectrometry.

Mass spectrometry imaging has become a hot research field owing to its ability to reflect the distribution of multiple metabolites in tissue. However, not all kinds of metabolites have great ionization efficiency in mass spectrometry imaging. The mass signals of low polar metabolites like monoglycerides and diglycerides may be seriously suppressed. Many strategies have been proposed to fix the problem, such as on-tissue derivatization and online derivatization. Also, some challenges were encountered when implementing these approaches. Herein, a platform coupled online quaternized derivatization and laser ablation carbon fiber ionization mass spectrometry imaging has been developed. The mass signals of monoglycerides and diglycerides were drastically increased in the platform, and high-quality mass images of these metabolites could be acquired readily. In the platform, metabolites were first desorbed by a laser and then reacted online with a derivatization reagent transmitted by carbon fiber ionization, which also undertook the postionization of derivatization products. Pyridine acted as the main derivatization reagent to target metabolites with hydroxyl groups. Remarkably, the derivatization reaction proceeded rapidly without any catalyst owing to the high energy provided by the laser. The mass images of eight monoglycerides and 21 diglycerides were achieved after applying the platform into human ovarian cancer tissues. Notably, a higher mass intensity of these glycerides was captured in cancerous tissues than in para-cancerous tissues, which might infer aberrations in glyceride metabolisms of cancerous tissues.

Effect of facemask oxygenation with and without positive pressure ventilation on gastric volume during anesthesia induction in patients undergoing laparoscopic cholecystectomy or partial hepatectomy: a randomized controlled trial.

BACKGROUND: Studies focusing on the relationship between gastric volume and facemask oxygenation without ventilation during apnea in anesthesia induction are scarce. This study compared the change in gastric volume during apnea in anesthesia induction using facemask ventilation and facemask oxygenation without ventilation in adults undergoing laparoscopic surgery. METHODS: In this prospective, randomized, double-blinded trial, 70 adults undergoing laparoscopic surgery under general anesthesia were divided into two groups to receive facemask oxygenation with and without ventilation for 60 seconds after loss of consciousness. Before anesthesia induction and after endotracheal intubation, the gastric antral cross-sectional area was measured with ultrasound imaging. Arterial blood gases were tested at baseline (T1), after preoxygenation (T2), after loss of consciousness (T3), and before and after endotracheal intubation (T4 and T5, respectively). RESULTS: Sixty patients were included (ventilation n = 30; non ventilation n = 30, 10 patients were excluded). The median [IQR] change of gastric antral cross-sectional area in ventilation group was significantly higher than in non ventilation group (0.83 [0.20 to 1.54] vs. 0.10 [- 0.11 to 0.56] cm2, P = 0.001). At T4 and T5, the PaO2 in ventilation group was significantly higher than in non ventilation group (T4: 391.83 ± 61.53 vs. 336.23 ± 74.99 mmHg, P < 0.01; T5: 364.00 ± 58.65 vs. 297.13 ± 86.95 mmHg, P < 0.01), while the PaCO2 in non ventilation group was significantly higher (T4: 46.57 ± 5.78 vs. 37.27 ± 6.10 mmHg, P < 0.01; T5: 48.77 ± 6.59 vs. 42.63 ± 6.03 mmHg, P < 0.01) and the pH value in non ventilation group was significantly lower (T4: 7.35 ± 0.029 vs 7.42 ± 0.047, P < 0.01; T5: 7.34 ± 0.033 vs 7.39 ± 0.044, P < 0.01). At T4, the HCO3- in non ventilation group was significantly higher (25.79 ± 2.36 vs. 23.98 ± 2.18 mmol l- 1, P < 0.01). CONCLUSIONS: During apnoea, the increase in gastric volume was milder in patients undergoing facemask oxygenation without ventilation than with positive pressure ventilation. TRIAL REGISTRATION: ChiCTR2100054193, 10/12/2021, Title: "Effect of positive pressure and non-positive pressure ventilation on gastric volume during induction of general anesthesia in laparoscopic surgery: a randomized controlled trial". Website: https://www.chictr.ogr.cn .

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.

Chiral Analysis of Lactate during Direct Contact Coculture by Single-Cell On-Probe Enzymatic Dehydrogenation Derivatization: Unraveling Metabolic Changes Caused by d-Lactate.

In vitro noncontact cell-based coculture models are frequently employed to study cell-to-cell communication. However, these models cannot accurately represent the complexity of in vivo signaling. d-Lactate is an unusual metabolite produced and released by cancer cells. The characterization of d-lactate is challenging as it shares the same mass but has much lower amounts compared with l-lactate. Herein, d-α-hydroxy acids were specifically recognized and dehydrogenated by d-α-hydroxy acid dehydrogenase. The dehydrogenation products were rapidly quaternized for enhancement of mass signals. An on-probe enzymatic dehydrogenation-derivatization method was proposed for chiral analysis of α-hydroxy acids at the single-cell level. It is a promising amplification methodology and affords over 3 orders of magnitude signal enhancement. Furthermore, direct contact coculture models were used to precisely mimic the tumor microenvironment and explore the communication between cancer and normal cells. Single-cell mass spectrometry (SCMS) was further applied to easily sample cell extracts and study the differences of the aspects of small molecule metabolism in cocultured cells. On the basis of direct contact coculture SCMS, several differential small molecule metabolites and differences of oxidative stress between cocultured and monocultured normal cells were successfully detected. Additionally, d-lactate was discovered as a valuable differential metabolite with application of the two developed methods. It may account for the cancer-associated metabolic behavior of normal cells. These changes could be relieved after d-lactate metabolism-related drug treatment. This discovery may promote the investigation of d-lactate metabolism, which may provide a novel direction for cancer therapy.

Highly Efficient Thermal Energy Storage Using a Hybrid Hypercrosslinked Polymer*.

In this work, an organic/inorganic hybrid polymer containing siloxyl functional groups was synthesized and applied to encapsulate phase change materials (PCMs). Owing to the mild conditions of the hypercrosslinking reaction, which only requires the addition of a catalytic amount of aqueous alkaline solution, both organic and inorganic PCMs are tolerated. It is noteworthy that the initial homogeneous state of the reaction mixture allowed the ultimate encapsulation rate of the PCMs and the uniform blending of the third nano-additives with the aim of thermal conductivity enhancement. Further study reveals that the presence of this hybrid hydrophobic polymer in a phase change composite endows the latter with a unique self-cleaning property. This novel PCM encapsulation protocol is suitable for nanoparticles including carbon-based nanomaterials, metal oxide nanoparticles, and inorganic oxide nanoparticles. A thermal conductivity enhancement of 600 % was achieved along with 93.7 % light-to-thermal conversion efficiency with a latent heat of 180 J g-1 without leakage.

Single-Cell On-Probe Derivatization-Noncontact Nanocarbon Fiber Ionization: Unraveling Cellular Heterogeneity of Fatty Alcohol and Sterol Metabolites.

Currently in single-cell mass spectrometry, the analysis of low-abundance cell metabolites such as fatty alcohols and sterols remains a challenge. In most research studies, single-cell samples are analyzed directly after sampling. However, this workflow may exclude many effective sample pretreatment methods such as derivatization for the improvement of detection sensitivity for specific cell metabolites in a single-cell sample. Metabolites in low abundance in a cell may not be detected. Herein on-probe derivatization coupled with noncontact nanocarbon fiber ionization is proposed for sensitive fatty alcohol and sterol metabolite analysis at the single-cell level. Fatty alcohol and sterol metabolites were rapidly quaternized by the single-cell on-probe derivatization method. The reaction products were directly ionized with no postreaction processing. Furthermore, a new ionization source for noncontact nanocarbon fiber ionization was developed to show good compatibility with dichloromethane, a low-polarity solvent used in on-probe derivatization. The quaternized fatty alcohols and sterols exhibited evidently enhanced ionization efficiency in mass spectra. In applications of the developed method, seven kinds of even-numbered-carbon fatty alcohols (C12-C22) and five kinds of sterols were detected in single L-02 and HepG2 cells. Then the L-02 and HepG2 cells were readily discriminated through principal component analysis. Additionally, a rough quantitative analysis of the detected fatty alcohols and sterols in single cells was performed. The mass intensities of fatty alcohols show a significant difference between L-02 and HepG2 cells while those of sterols remain stable.

Simultaneous Analysis of Fatty Alcohols, Fatty Aldehydes, and Sterols in Thyroid Tissues by Electrospray Ionization-Ion Mobility-Mass Spectrometry Based on Charge Derivatization.

In this work, we developed a rapid and high-sensitivity method for simultaneous analyses of fatty alcohols, fatty aldehydes, and sterols by combining the optimized derivatization reaction with electrospray ionization-ion mobility-mass spectrometry (ESI-IM-MS). Pyridine and thionyl chloride were used as derivatization reagents as they were easily removed after the derivatization reaction and could generate permanently charged tags on different functional groups including hydroxyls and aldehydes. Through this one-step derivatization reaction, the sensitivity of detection for fatty alcohols, fatty aldehydes, and sterols was significantly increased. Moreover, the introduction of ion mobility spectrometry (IMS), offering an additional resolution power, ensured more sensitive and accurate detection of derivative products without increasing analytical time. Being connected with high-performance liquid chromatography, more than 15 kinds of compounds were analyzed within 4 min. Relative quantification using peak intensity ratios between d0-/d5-labeled ions were subsequently applied for analyzing these 15 kinds of compounds in human thyroid carcinoma and para-carcinoma tissues. The results showed significant differences in content of some analytes between these two kinds of tissues (p < 0.05). The correlations between most of the analytes in thyroid carcinoma tissues are better than the correlations in para-carcinoma tissues.

Analysis and inspection techniques for mouse liver injury based on terahertz spectroscopy.

At present, researchers are exploring biological tissue detection method using terahertz techniques. In this paper, techniques to inspect mouse liver injury by using terahertz spectroscopy were studied. The boxplots were applied to remove abnormal data, and the maximal information coefficient was employed to select crucial features from the absorption coefficient and refractive index spectra. Random Forests and AdaBoost were applied to recognize different levels of liver injury. We found that AdaBoost had better performance on low-level injury classification. This work suggests that terahertz techniques have the potential to detect liver injury at an early stage and evaluate liver treatment strategies.

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.

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.

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.

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.