In Silico Design and Synthesis of Antifungal Peptides Guided by Quantitative Antifungal Activity.

Antifungal peptides (AFPs) are emerging as promising candidates for advanced antifungal therapies because of their broad-spectrum efficacy and reduced resistance development. In silico design of AFPs, however, remains challenging, due to the lack of an efficient and well-validated quantitative assessment of antifungal activity. This study introduced an AFP design approach that leverages an innovative quantitative metric, named the antifungal index (AFI), through a three-step process, i.e., segmentation, single-point mutation, and global multipoint optimization. An exhaustive search of 100 putative AFP sequences indicated that random modifications without guidance only have a 5.97-20.24% chance of enhancing antifungal activity. Analysis of the search results revealed that (1) N-terminus truncation is more effective in enhancing antifungal activity than the modifications at the C-terminus or both ends, (2) introducing the amino acids within the 10-60% sequence region that enhance aromaticity and hydrophobicity are more effective in increasing antifungal efficacy, and (3) incorporating alanine, cysteine, and phenylalanine during multiple point mutations has a synergistic effect on enhancing antifungal activity. Subsequently, 28 designed peptides were synthesized and tested against four typical fungal strains. The success rate for developing promising AFPs, with a minimal inhibitory concentration of ≤5.00 µM, was an impressive 82.14%. The predictive and design tool is accessible at https://antifungipept.chemoinfolab.com.

Efficient Deep Spiking Multilayer Perceptrons With Multiplication-Free Inference.

Advancements in adapting deep convolution architectures for spiking neural networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of multiplication-free inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposes limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in multilayer perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization (BN) to retain MFI compatibility and introduce a spiking patch encoding (SPE) layer to enhance local feature extraction capabilities. As a result, we establish an efficient multistage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pretraining or sophisticated SNN training techniques, our network secures a top-one accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-one accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells.

Determination of organic acids for predicting sourness intensity of tea beverage by liquid chromatography-tandem mass spectrometry and chemometrics methods.

The contents of organic acids (OAs) in tea beverage and their relationship with taste intensity have not been fully understood. In this work, a rapid (10 min for a single run) and sensitive (limits of quantification: 0.0044-0.4486 µg/mL) method was developed and validated for the simultaneous determination of 17 OAs in four types of tea, based on liquid chromatography-tandem mass spectrometry with multiple reaction monitoring mode. The contents of 17 OAs in 96 tea samples were measured at levels between 0.01 and 11.80 g/kg (dried weight). Quinic acid, citric acid, and malic acid were determined as the major OAs in green, black, and raw pu-erh teas, while oxalic acid and tartaric acid exhibited the highest contents in ripe pu-erh tea. Taking the OAs composition as input features, a partial least squares regression model was proposed to predict the sourness intensity of tea beverages. The model achieved a root-mean-square error of 0.58 and a coefficient of determination of 0.84 for the testing set. The proposed model provides a theoretical way to evaluate the sensory quality of tea infusion based on its chemical composition.

Electronic structures and quantum capacitance of twisted bilayer graphene with defects based on three-band tight-binding model.

Twisted bilayer graphene (tBLG) with C vacancies would greatly improve the density of states (DOS) around the Fermi level (EF) and quantum capacitance; however, the single-band tight-binding model only considering pz orbitals cannot accurately capture the low-energy physics of tBLG with C vacancies. In this work, a three-band tight-binding model containing three p orbitals of C atoms is proposed to explore the modulation mechanism of C vacancies on the DOS and quantum capacitance of tBLG. We first obtain the hopping integral parameters of the three-band tight-binding model, and then explore the electronic structures and the quantum capacitance of tBLG at a twisting angle of θ = 1.47° under different C vacancy concentrations. The impurity states contributed by C atoms with dangling bonds located around the EF and the interlayer hopping interaction could induce band splitting of the impurity states. Therefore, compared with the quantum capacitance of pristine tBLG (∼18.82 µF cm-2) at zero bias, the quantum capacitance is improved to ∼172.76 µF cm-2 at zero bias, and the working window with relatively large quantum capacitance in the low-voltage range is broadened in tBLG with C vacancies due to the enhanced DOS around the EF. Moreover, the quantum capacitance of tBLG is further increased at zero bias with an increase of the C vacancy concentration induced by more impurity states. These findings not only provide a suitable multi-band tight-binding model to describe tBLG with C vacancies but also offer theoretical insight for designing electrode candidates for low-power consumption devices with improved quantum capacitance.

Copy number gain of FAM131B-AS2 promotes the progression of glioblastoma by mitigating replication stress.

BACKGROUND: Glioblastoma (GBM) is characterized by chromosome 7 copy number gains, notably 7q34, potentially contributing to therapeutic resistance, yet the underlying oncogenes have not been fully characterized. Pertinently, the significance of long noncoding RNAs (lncRNAs) in this context has gained attention, necessitating further exploration. METHODS: FAM131B-AS2 was quantified in GBM samples and cells using qPCR. Overexpression and knockdown of FAM131B-AS2 in GBM cells were used to study its functions in vivo and in vitro. The mechanisms of FAM131B-AS2 were studied using RNA-seq, qPCR, Western blotting, RNA pull-down, coimmunoprecipitation assays, and mass spectrometry analysis. The phenotypic changes that resulted from FAM131B-AS2 variation were evaluated through CCK8 assay, EdU assay, comet assay, and immunofluorescence. RESULTS: Our analysis of 149 primary GBM patients identified FAM131B-AS2, a lncRNA located in the 7q34 region, whose upregulation predicts poor survival. Mechanistically, FAM131B-AS2 is a crucial regulator of the replication stress response, stabilizing RPA1 through recruitment of USP7 and activating the ATR pathway to protect single-stranded DNA from breakage. Furthermore, FAM131B-AS2 overexpression inhibited CD8+ T-cell infiltration, while FAM131B-AS2 inhibition activated the cGAS-STING pathway, increasing lymphocyte infiltration and improving the response to immune checkpoint inhibitors. CONCLUSION: FAM131B-AS2 emerges as a promising indicator for adjuvant therapy response and could also be a viable candidate for combined immunotherapies against GBMs.

Neuronal Activity Promotes Glioma Progression by Inducing Proneural-to-Mesenchymal Transition in Glioma Stem Cells.

Neuronal activity can drive progression of high-grade glioma by mediating mitogen production and neuron-glioma synaptic communications. Glioma stem cells (GSC) also play a significant role in progression, therapy resistance, and recurrence in glioma, which implicates potential cross-talk between neuronal activity and GSC biology. Here, we manipulated neuronal activity using chemogenetics in vitro and in vivo to study how it influences GSCs. Neuronal activity supported glioblastoma (GBM) progression and radioresistance through exosome-induced proneural-to-mesenchymal transition (PMT) of GSCs. Molecularly, neuronal activation led to elevated miR-184-3p in neuron-derived exosomes that were taken up by GSCs and reduced the mRNA N6-methyladenosine (m6A) levels by inhibiting RBM15 expression. RBM15 deficiency decreased m6A modification of DLG3 mRNA and subsequently induced GSC PMT by activating the STAT3 pathway. Loss of miR-184-3p in cortical neurons reduced GSC xenograft growth, even when neurons were activated. Levetiracetam, an antiepileptic drug, reduced the neuronal production of miR-184-3p-enriched exosomes, inhibited GSC PMT, and increased radiosensitivity of tumors to prolong survival in xenograft mouse models. Together, these findings indicate that exosomes derived from active neurons promote GBM progression and radioresistance by inducing PMT of GSCs. SIGNIFICANCE: Active neurons secrete exosomes enriched with miR-184-3p that promote glioblastoma progression and radioresistance by driving the proneural-to-mesenchymal transition in glioma stem cells, which can be reversed by antiseizure medication levetiracetam.

Hypoxia-Induced Neuronal Activity in Glioma Patients Polarizes Microglia by Potentiating RNA m6A Demethylation.

PURPOSE: Neuronal activity in the brain has been reported to promote the malignant progression of glioma cells via nonsynaptic paracrine and electrical synaptic integration mechanisms. However, the interaction between neuronal activity and the immune microenvironment in glioblastoma (GBM) remains largely unclear. EXPERIMENTAL DESIGN: By applying chemogenetic techniques, we enhanced and inhibited neuronal activity in vitro and in a mouse model to study how neuronal activity regulates microglial polarization and affects GBM progression. RESULTS: We demonstrate that hypoxia drove glioma stem cells (GSC) to produce higher levels of glutamate, which activated local neurons. Neuronal activity promoted GBM progression by facilitating microglial M2 polarization through enriching miR-200c-3p in neuron-derived exosomes, which decreased the expression of the m6A writer zinc finger CCCH-type containing 13 (ZC3H13) in microglia, impairing methylation of dual specificity phosphatase 9 (DUSP9) mRNA. Downregulation of DUSP9 promoted ERK pathway activation, which subsequently induced microglial M2 polarization. In the mouse model, cortical neuronal activation promoted microglial M2 polarization whereas cortical neuronal inhibition decreased microglial M2 polarization in GBM xenografts. miR-200c-3p knockdown in cortical neurons impaired microglial M2 polarization and GBM xenograft growth, even when cortical neurons were activated. Treatment with the anti-seizure medication levetiracetam impaired neuronal activation and subsequently reduced neuron-mediated microglial M2 polarization. CONCLUSIONS: These findings indicated that hypoxic GSC-induced neuron activation promotes GBM progression by polarizing microglia via the exosomal miR-200c-3p/ZC3H13/DUSP9/p-ERK pathway. Levetiracetam, an antiepileptic drug, blocks the abnormal activation of neurons in GBM and impairs activity-dependent GBM progression. See related commentary by Cui et al., p. 1073.

SOC Estimation of Vanadium Redox Flow Batteries Based on the ISCSO-ELM Algorithm.

This study focuses on the stage of charge (SOC) estimation for vanadium redox flow batteries (VFBs), establishing an electrochemical model that provides parameters, including ion concentration. Second, considering the capacity decay of VFBs, an extreme learning machine (ELM) combined with an improved sand cat swarm optimization algorithm, named ISCSO-ELM, is integrated with SOC estimation to predict the battery's SOC more effectively.

Coal Classification Based on Reflection Spectroscopy and the IAT-TELM Algorithm.

As a principal energy globally, coal's quality and variety critically influence the effectiveness of industrial processes. Different coal types cater to specific industrial requirements due to their unique attributes. Traditional methods for coal classification, typically relying on manual examination and chemical assays, lack efficiency and fail to offer consistent accuracy. Addressing these challenges, this work introduces an algorithm based on the reflectance spectrum of coal and machine learning. This method approach facilitates the rapid and accurate classification of coal types through the analysis of coal spectral data. First, the reflection spectra of three types of coal, namely, bituminous coal, anthracite, and lignite, were collected and preprocessed. Second, a model utilizing two hidden layer extreme learning machine (TELM) and affine transformation function is introduced, which is called affine transformation function TELM (AT-TELM). AT-TELM introduces an affine transformation function on the basis of TELM, so that the hidden layer output satisfies the maximum entropy principle and improves the recognition performance of the model. Third, we improve AT-TELM by optimizing the weight matrix and bias of AT-TELM to address the issue of highly skewed distribution caused by randomly assigned weights and biases. The method is named the improved affine transformation function (IAT-TELM). The experimental findings demonstrate that IAT-TELM achieves a remarkable coal classification accuracy of 97.8%, offering a cost-effective, rapid, and precise method for coal classification.

Effects of provisioning on the activity budget and foraging strategies of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in the Baima Snow Mountain Nature Reserve, Yunnan, China.

Provisioning can significantly affect the ranging patterns, foraging strategies, and time budget of wild primates. In this study, we document for the first time, the effects of provisioning on the activity budget and foraging effort in an Asian colobine. Over 3-years, we used an instantaneous scanning method at 10-min intervals to collect data on the activity budget of a semiprovisioned breeding band (SPB) of black-and-white snub-nosed monkeys (Rhinopithecus bieti) (42-70 individuals) at Xiangguqing (Tacheng), Yunnan, China. We then compared the effects of provisioning in our study band with published data on a sympatric wild nonprovisioned breeding band (NPB) of R. bieti (ca. 360 monkeys) at the same field site. The SPB spent 25.6% of their daytime feeding, 17.1% traveling, 46.9% resting, and 10.3% socializing. In comparison, the NPB devoted more time to feeding (34.9%) and socializing (14.1%), less time to resting (31.3%), and was characterized by a greater foraging effort (1.74 versus 0.96, foraging effort = (feeding + traveling)/resting; see Methods). There was no difference between bands in the proportion of their activity budget devoted to traveling (15.7% vs. 17.1%). In addition, the SPB exhibited a more consistent activity budget and foraging effort across all seasons of the year compared to the NPB. These findings suggest that the distribution, availability, and productivity of naturally occurring feeding sites is a major determinant of the behavioral strategies and activity budget of R. bieti. Finally, a comparison of our results with data on six nonprovisioned R. bieti bands indicates that caution must be raised in meta-analyses or intraspecific comparisons of primate behavioral ecology that contain data generated from both provisioned and nonprovisioned groups.

Mesenchymal stem cells, as glioma exosomal immunosuppressive signal multipliers, enhance MDSCs immunosuppressive activity through the miR-21/SP1/DNMT1 positive feedback loop.

BACKGROUND: The immunosuppressive microenvironment in glioma induces immunotherapy resistance and is associated with poor prognosis. Glioma-associated mesenchymal stem cells (GA-MSCs) play an important role in the formation of the immunosuppressive microenvironment, but the mechanism is still not clear. RESULTS: We found that GA-MSCs promoted the expression of CD73, an ectonucleotidase that drives immunosuppressive microenvironment maintenance by generating adenosine, on myeloid-derived suppressor cells (MDSCs) through immunosuppressive exosomal miR-21 signaling. This process was similar to the immunosuppressive signaling mediated by glioma exosomal miR-21 but more intense. Further study showed that the miR-21/SP1/DNMT1 positive feedback loop in MSCs triggered by glioma exosomal CD44 upregulated MSC exosomal miR-21 expression, amplifying the glioma exosomal immunosuppressive signal. Modified dendritic cell-derived exosomes (Dex) carrying miR-21 inhibitors could target GA-MSCs and reduce CD73 expression on MDSCs, synergizing with anti-PD-1 monoclonal antibody (mAb). CONCLUSIONS: Overall, this work reveals the critical role of MSCs in the glioma microenvironment as signal multipliers to enhance immunosuppressive signaling of glioma exosomes, and disrupting the positive feedback loop in MSCs with modified Dex could improve PD-1 blockade therapy.

PFCE2: A versatile parameter-free calibration enhancement framework for near-infrared spectroscopy.

Near-infrared (NIR) spectroscopy is a widely used technique for chemical analysis, but it has faced challenges of calibration transfer, maintenance, and enhancement among different instruments and conditions. The parameter-free calibration enhancement (PFCE) framework was developed to address these challenges with non-supervised (NS), semi-supervised (SS), and full-supervised (FS) methods. This study presented PFCE2, an updated version of the PFCE framework that incorporates two new constraints and a new method to improve the robustness and efficiency of calibration enhancement. First, normalized L2 and L1 constraints were introduced to replace the correlation coefficient (Corr) constraint used in the original PFCE. These constraints preserve the parameter-free feature of PFCE and impose smoothness or sparsity on the model coefficients. Second, multitask PFCE (MT-PFCE) was proposed within the framework to address the calibration enhancement among multiple instruments, enabling the framework to be versatile for all possible calibration transfer situations. Demonstrations conducted on three NIR datasets of tablets, plant leaves, and corn showed that the PFCE methods with the new L2 and L1 constraints can result in more accurate and robust predictions than the Corr constraint, especially when the standard sample size is small. Moreover, MT-PFCE could refine all models in the involved scenarios at once, leading to significant enhancement in model performance, compared to the original PFCE method with the same data requirements. Finally, the applicable situations of the PFCE framework and other analogous calibration transfer methods were summarized, facilitating users to choose suitable methods for their application. The source codes written in both MATLAB and Python are available at https://github.com/JinZhangLab/PFCE and https://pypi.org/project/pynir/, respectively.

Correction: Theoretical predictions and experimental verifications of SERS detection in colorants.

[This corrects the article DOI: 10.1039/D3RA01584J.].

SPI1-mediated MIR222HG transcription promotes proneural-to-mesenchymal transition of glioma stem cells and immunosuppressive polarization of macrophages.

Background: Glioma stem cells (GSCs) are a key factor in glioblastoma (GBM) development and treatment resistance. GSCs can be divided into the mesenchymal (MES) and proneural (PN) subtypes, and these two subtypes of GSCs can undergo interconversion under certain conditions. MES GSCs have higher malignancy and radioresistance and are closely associated with an immunosuppressive microenvironment. Long noncoding RNAs (lncRNAs) play a broad role in GBM, while the role of GSCs subtype remains unknown. Methods: We performed RNA sequencing to explore the lncRNA expression profile in MES- and PN-subtype GBM tissues. The biological function of a host gene-MIR222HG-in GBM development was confirmed in vitro and in vivo. Specifically, RNA sequencing, RNA pulldown, mass spectrometry, RIP, ChIP, luciferase reporter assays and Co-IP were performed. Results: MIR222HG, the expression of which can be induced by SPI1, has high levels in MES GBM tissues. Functionally, we demonstrated that MIR222HG promotes the MES transition and radioresistance in GSCs in vivo and in vitro. Mechanistically, MIR222HG can bind to the YWHAE/HDAC5 complex to promote the MES transition of GSCs through H4 deacetylation. Moreover, cotranscribed miR221 and miR222 can be delivered to macrophages via exosomes to target SOCS3, causing immunosuppressive polarization. Finally, PLX-4720 sensitivity is associated with SPI1 expression and acts on MES GSCs to enhance radiosensitivity. Conclusions: This study demonstrates that targeting SPI1 to block transcription of the MIR222HG cluster helps to reduce radioresistance and combat the immunosuppressive microenvironment in GBM. PLX-4720 is a potential GBM drug and radiosensitizer.

Comparison of mNGS and conventional culture in non-organ transplant critically ill patients supported by ECMO: a single-center study.

Objectives: Infection is one of the important causes of death in intensive care unit (ICU) patients. At present, there are few articles focused on the detailed analysis of pathogenic microorganisms detected in different therapy periods of critically ill patients supported by extracorporeal membrane oxygenation (ECMO). Methods: From October 2020 to October 2022, ECMO-assisted patients who underwent multiple times of both metagenomic next-generation sequencing (mNGS) test and conventional culture were enrolled continuously in the First Affiliated Hospital of Zhengzhou University. The baseline data, laboratory test results, and pathogenic microorganisms detected by mNGS and traditional culture in different time periods were recorded and analyzed. Results: In the present study, 62 patients were included finally. According to whether the patients survived at discharge, they were divided into the survivor group (n = 24) and the non-survivor group (n = 38). Then, according to the different types of ECMO support, they were divided into the veno-venous ECMO (VV ECMO) group (n = 43) and the veno-arterial ECMO (VA ECMO) group (n = 19). The summit period of specimens of traditional culture and mNGS detection of ECMO patients was 7 days after admission, and the largest number of specimens of surviving patients appeared after ECMO withdrawal. The total number of traditional culture specimens was 1,249, the positive rate was 30.4% (380/1,249), and the positive rate of mNGS was 79.6% (82/103). A total of 28 kinds of pathogenic microorganisms were cultured from conventional culture, and 58 kinds of pathogenic microorganisms were detected by mNGS, including Mycobacterium, Rickettsia, and Chlamydia psittaci. In conventional culture, the most frequent Gram-negative bacteria, Gram-positive bacteria, and fungi were Klebsiella pneumoniae, Corynebacterium striatum, and Candida glabrata, and those with the highest frequency of occurrence in mNGS detection were Acinetobacter baumannii, Enterococcus faecium, and Aspergillus flavus. Conclusions: Throughout the whole treatment process, different kinds of suspicious biological specimens of high-infection-risk ICU patients supported by ECMO should undergo both mNGS detection and traditional culture early and repeatedly.

Theoretical predictions and experimental verifications of SERS detection in colorants.

Synthetic colorants added during food processing not only fail to provide nutrients, but also can be harmful to human health when used in excess. To establish a simple, convenient, rapid and low-cost surface-enhanced Raman spectroscopy (SERS) detection method for colorants, an active surface-enhanced substrate of colloidal gold nanoparticles (AuNPs) was prepared in this study. The density functional theory (DFT) method of B3LYP with 6-31G(d) was applied to determine the theoretical Raman spectra of erythrosine, basic orange 2, 21 and 22, and to attribute their characteristic spectral peaks. The SERS spectra of the four colorants were pre-processed using local least squares (LLS) and morphological weighted penalized least squares (MWPLS), and multiple linear regression (MLR) models were established to quantify the four colorants in beverages. The results showed that the prepared AuNPs with a particle size of about 50 nm were reproducible and stable, with a good enhancement of the SERS spectrum of rhodamine 6G at 10-8 mol L-1. The theoretical Raman frequencies were in good agreement with the experimental Raman frequencies, and the peak position differences of the main characteristic peaks of the four colorants were within 20 cm-1. The MLR calibration models for the concentrations of the four colorants showed relative errors of prediction (REP) of 2.97-8.96%, root mean square errors of prediction (RMSEP) of 0.03-0.94, R2 of 0.973-0.999, and limits of detection of 0.06 µg mL-1. The present method could be used to quantify erythrosine, basic orange 2, 21, and 22, revealing its wide range of applications in food safety.

Trimanganese Tetroxide Nanozyme protects Cartilage against Degeneration by Reducing Oxidative Stress in Osteoarthritis.

Osteoarthritis, a chronic degenerative cartilage disease, is the leading cause of movement disorders among humans. Although the specific pathogenesis and associated mechanisms remain unclear, oxidative stress-induced metabolic imbalance in chondrocytes plays a crucial role in the occurrence and development of osteoarthritis. In this study, a trimanganese tetroxide (Mn3 O4 ) nanozyme with superoxide dismutase (SOD)-like and catalase (CAT)-like activities is designed to reduce oxidative stress-induced damage and its therapeutic effect is investigated. In vitro, Mn3 O4 nanozymes are confirmed to reprogram both the imbalance of metabolism in chondrocytes and the uncontrolled inflammatory response stimulated by hydrogen peroxide. In vivo, a cross-linked chondroitin sulfate (CS) hydrogel is designed as a substrate for Mn3 O4 nanozymes to treat osteoarthritis in mouse models. As a result, even in the early stage of OA (4 weeks), the therapeutic effect of the Mn3 O4 @CS hydrogel is observed in both cartilage metabolism and inflammation. Moreover, the Mn3 O4 @CS hydrogel maintained its therapeutic effects for at least 7 days, thus revealing a broad scope for future clinical applications. In conclusion, these results suggest that the Mn3 O4 @CS hydrogel is a potentially effective therapeutic treatment for osteoarthritis, and a novel therapeutic strategy for osteoarthritis based on nanozymes is proposed.

Method comparison of sample pretreatment and discovery of differential compositions of natural flavors and fragrances for quality analysis by using chemometric tools.

Natural flavors and fragrances or their extracts have been widely used in a large variety of areas, including food, cosmetic, and tobacco industrial processes, among others. The compositions and intrinsic attributes of flavors and fragrances were related to many factors, such as species, geographical origin, planting environment, storage condition, processing method, and so on. This not only increased the difficulty in analyzing the product quality of flavors and fragrances, but also challenged the idea of "quality-by-design (QbD)". This work proposed an integrated strategy for precise discovery of differential compounds among different classes and subsequent quality analysis of complex samples through flavors and fragrances used in tobacco industry as examples. Three pretreatment methods were first inspected to effectively characterize the sample compositions, including direct injection (DI), thermal desorption (TD), and stir bar sorptive extraction (SBSE)-TD, coupled with gas chromatography-mass spectrometry (GC-MS) analysis to obtain characteristic information of samples of flavors and fragrances. Then, principal component analysis (PCA) was applied to discover the relation and difference between chromatographic fingerprints and peak table data once significant components were recognized in a holistic manner. Model population analysis (MPA) was then used to quantitatively extract the characteristic chemicals representing the quality differences among different classes of samples. Some differential marker compounds were discovered for difference analysis, including benzyl alcohol, latin acid, l-menthol acid, decanoic acid ethyl ester, vanillin, trans-o-coumaric acid, benzyl benzoate, and so on. Furthermore, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were respectively applied to construct multivariate models for evaluation of quality differences and variations. It was found that the accuracy attains to 100% for sample classification. With the help of optimal sample pretreatment technique and chemometric methods, the strategy for quality analysis and difference discovery proposed in this work can be widely delivered to more areas of complex plants with good interpretability and high accuracy.

Systematic analysis of the aberrances and functional implications of cuproptosis in cancer.

Cuproptosis is a novel form of cell death driven by a copper-dependent proteotoxic stress response whose comprehensive landscape in tumors remains unclear. Here, we comprehensively characterized cuproptosis-related genes (CRGs) across 33 cancers using multi-omic data from The Cancer Genome Atlas (TCGA), showing complicated and diverse results in different cancers. We also explored the relationships between CRGs and cancer metabolic patterns, pathway activity, and tumor microenvironment (TME), suggesting that they played critical roles in tumor progression and TME cell infiltration. We further established the cuproptosis potential index (CPI) to reveal the functional roles of cuproptosis, and characterized multi-omic molecular features associated with cuproptosis. In clinical applications, we performed a combined analysis of the sensitivity of CRGs and CPI to drug response and immunotherapy. This study provides a rich resource for understanding cuproptosis, offering a broad molecular perspective for future functional and therapeutic studies of multiple cancer pathways mediated by cuproptosis.

Opportunities and challenges related to ferroptosis in glioma and neuroblastoma.

A newly identified form of cell death known as ferroptosis is characterized by the peroxidation of lipids in response to iron. Rapid progress in research on ferroptosis in glioma and neuroblastoma has promoted the exploitation of ferroptosis in related therapy. This manuscript provides a review of the findings on ferroptosis-related therapy in glioblastoma and neuroblastoma and outlines the mechanisms involved in ferroptosis in glioma and neuroblastoma. We summarize some recent data on traditional drugs, natural compounds and nanomedicines used as ferroptosis inducers in glioma and neuroblastoma, as well as some bioinformatic analyses of genes involved in ferroptosis. Moreover, we summarize some data on the associations of ferroptosis with the tumor immunotherapy and TMZ drug resistance. Finally, we discuss future directions for ferroptosis research in glioma and neuroblastoma and currently unresolved issues.