Molecular Structural Features Research Articles

Role of WAVE3 as an of actin binding protein in the pathology of triple negative breast cancer.

Breast cancer, a prevalent global health concern, has sparked extensive research efforts, particularly focusing on triple negative breast cancer (TNBC), a subtype lacking estrogen receptor (ER), progesterone receptor, and epidermal growth factor receptor. TNBC's aggressive nature and resistance to hormone-based therapies heightens the risk of tumor progression and recurrence. Actin-binding proteins, specifically WAVE3 from the Wiskott-Aldrich syndrome protein (WASP) family, have emerged as major drivers in understanding TNBC biology. This review delves into the intricate molecular makeup of TNBC, shedding light on actin's fundamental role in cellular processes. Actin, a structural element in the cytoskeleton, regulates various cellular pathways essential for homeostasis. Its dynamic nature enables functions such as cell migration, motility, intracellular transport, cell division, and signal transduction. Actin-binding proteins, including WAVE3, play pivotal roles in these processes. WAVE3, a member of the WASP family, remains the focus of this review due to its potential involvement in TNBC progression. While actin-binding proteins are studied for their roles in healthy cellular cycles, their significance in TNBC remains underexplored. This review aims to discuss WAVE3's impact on TNBC, exploring its molecular makeup, functions, and significance in tumor progression. The intricate structure of WAVE3, featuring elements like the verprolin-cofilin-acidic domain and regulatory elements, plays a crucial role in regulating actin dynamics. Dysregulation of WAVE3 in TNBC has been linked to enhanced cell migration, invasion, extracellular matrix remodeling, epithelial-mesenchymal transition, tumor proliferation, and therapeutic resistance. Understanding the role of actin-binding proteins in cancer biology has potential clinical implications, making them potential prognostic biomarkers and promising therapeutic targets. The review emphasizes the need for further research into actin-binding proteins' clinical applications, diagnostic value, and therapeutic interventions. In conclusion, this comprehensive review explores the complex interplay between actin and actin-binding proteins, with special emphasis on WAVE3, in the context of TNBC. By unraveling the molecular intricacies, structural characteristics, and functional significance, the review paves the way for future research directions, clinical applications, and potential therapeutic strategies in the challenging landscape of TNBC.

A dual-modality complex-valued fusion method for predicting side effects of drug-drug interactions based on graph neural network.

Predicting potential side effects of drug-drug interactions (DDIs), which is a major concern in clinical treatment, can increase therapeutic efficacy. In recent studies, how to use the multi-modal drug features is important for DDI prediction. Thus, it remains a challenge to explore an efficient computational method to achieve the feature fusion cross- and intra-modality. In this paper, we propose a dual-modality complex-valued fusion method (DMCF-DDI) for predicting the side effects of DDIs, using the form and properties of complex-vector to enhance the representations of DDIs. Firstly, DMCF-DDI applies two Graph Convolutional Network (GCN) encoders to learn molecular structure and topological features from fingerprint and knowledge graphs, respectively. Secondly, an asymmetric skip connection (ASC) uses distinct semantic-level features to construct the complex-valued drug pair representations (DPRs). Then, the complex-vector multiplication is used as a fusion operator to obtain the fine-grained DPRs. Finally, we calculate the prediction probability of DDIs by Hermitian inner product in the complex space. Compared with other methods, DMCF-DDI achieves superior performance in all situations using a fusion operator with the lowest parameter numbers. For the case study, we select six diseases and common side effects in clinical treatment to verify identification ability of our model. We also prove the advantage of ASC and complex-valued fusion can achieve to align the cross-modal fused positive DPRs through a comprehensive analysis on the phase-modulus distribution histogram of DPRs. In the end, we explain the reason for alignment based on the similarity of features and node neighbors.

A deep learning drug screening framework for integrating local-global characteristics: A novel attempt for limited data

At the beginning of the "Disease X" outbreak, drug discovery and development are often challenged by insufficient and unbalanced data. To address this problem and maximize the information value of limited data, we propose a drug screening model, LGCNN, based on convolutional neural network (CNN), which enables rapid drug screening by integrating features of drug molecular structures and drug-target interactions at both local and global (LG) levels. Experimental results show that LGCNN exhibits better performance compared to other state-of-the-art classification methods under limited data. In addition, LGCNN was applied to anti-SARS-CoV-2 drug screening to realize therapeutic drug mining against COVID-19. LGCNN transcends the limitations of traditional models for predicting interactions between single drug targets and shows new advantages in predicting multi-target drug-target interactions. Notably, the cross-coronavirus generalizability of the model is also implied by the analysis of targets, drugs, and mechanisms in the prediction results. In conclusion, LGCNN provides new ideas and methods for rapid drug screening in emergency situations where data are scarce.

The perception interaction of complex fruits aroma system: including binary, ternary, quaternary, and with different chain length, functional groups and substituent positions compounds

The perceptual interactions between key aroma compounds (binary, ternary, and quaternary) of fruits were comprehensively evaluated using thresholds, odor activity values, Feller’s additive model (S-curve), σ-τ diagram, and U model. Meanwhile, support vector machine (SVM) was initially introduced to evaluate and compare the 5 methods for selecting the most appropriate approach for investigating fruits aroma interaction. Finally, the effects of aroma molecular structure characteristics (chain length, functional group and substituent site) on the interaction was investigated. The results showed that S-curve, σ-τ diagram and U model are suitable for the binary and ternary systems, and σ-τ diagram and U model are suitable for quaternary systems. Furthermore, the functional groups strongly (P < 0.05) influenced the perception interactions. Especially, the greater the difference in carbon chain length, the more significant the synergistic effect of aldehydes and alcohols with similar chains, and the substituent site had no significant effect on aroma interactions.

The perception interaction of complex fruits aroma system: Including binary, ternary, quaternary, and with different chain length, functional groups and substituent positions compounds

The perceptual interactions between key aroma compounds (binary, ternary, and quaternary) of fruits were comprehensively evaluated using thresholds, odor activity values, Feller's additive model (S-curve), σ-τ diagram, and U model. Meanwhile, support vector machine (SVM) was initially introduced to evaluate and compare the 5 methods for selecting the most appropriate approach for investigating fruits aroma interaction. Finally, the effects of aroma molecular structure characteristics (chain length, functional group and substituent site) on the interaction was investigated. The results showed that S-curve, σ-τ diagram and U model are suitable for the binary and ternary systems, and σ-τ diagram and U model are suitable for quaternary systems. Furthermore, the functional groups strongly (P < 0.05) influenced the perception interactions. Especially, the greater the difference in carbon chain length, the more significant the synergistic effect of aldehydes and alcohols with similar chains, and the substituent site had no significant effect on aroma interactions.

Recent Advances in Polysaccharides from Chaenomeles speciosa (Sweet) Nakai.: Extraction, Purification, Structural Characteristics, Health Benefits, and Applications.

This article systematically reviews the extraction and purification methods, structural characteristics, structure-activity relationship, and health benefits of C. speciosa polysaccharides, and their potential application in food, medicine, functional products, and feed, in order to provide a useful reference for future research. Chaenomeles speciosa (Sweet) Nakai. has attracted the attention of health consumers and medical researchers as a traditional Chinese medicine with edible, medicinal, and nutritional benefits. According to this study, C. speciosa polysaccharides have significant health benefits, such as anti-diaetic, anti-inflammatory and analgesic, anti-tumor, and immunomodulatory effects. Researchers determined the molecular weight, structural characteristics, and monosaccharide composition and ratio of C. speciosa polysaccharides by water extraction and alcohol precipitation. This study will lay a solid foundation for further optimization of the extraction process of C. speciosa polysaccharides and the development of their products. As an active ingredient with high value, C. speciosa polysaccharides are worthy of further study and full development. C. speciosa polysaccharides should be further explored in the future, to innovate their extraction methods, enrich their types and biological activities, and lay a solid foundation for further research and development of products containing polysaccharides that are beneficial to the human body.

Deep graph contrastive learning model for drug-drug interaction prediction.

Drug-drug interaction (DDI) is the combined effects of multiple drugs taken together, which can either enhance or reduce each other's efficacy. Thus, drug interaction analysis plays an important role in improving treatment effectiveness and patient safety. It has become a new challenge to use computational methods to accelerate drug interaction time and reduce its cost-effectiveness. The existing methods often do not fully explore the relationship between the structural information and the functional information of drug molecules, resulting in low prediction accuracy for drug interactions, poor generalization, and other issues. In this paper, we propose a novel method, which is a deep graph contrastive learning model for drug-drug interaction prediction (DeepGCL for brevity). DeepGCL incorporates a contrastive learning component to enhance the consistency of information between different views (molecular structure and interaction network), which means that the DeepGCL model predicts drug interactions by integrating molecular structure features and interaction network topology features. Experimental results show that DeepGCL achieves better performance than other methods in all datasets. Moreover, we conducted many experiments to analyze the necessity of each component of the model and the robustness of the model, which also showed promising results. The source code of DeepGCL is freely available at https://github.com/jzysj/DeepGCL.

Study on the Mass Spectrometry Cleavage Pattern of Quinolone Antibiotics.

Quinolone antibiotics are extensively used clinically for human treatment and in agriculture. However, improper and excessive use can lead to the persistence of quinolone residues in animal tissues, potentially accumulating in the human body and posing health risks. Investigating the correlation between mass spectrometry cleavage patterns and molecular structural features enhances the analytical framework for detecting trace or unknown impurities in quinolones. To collect data, we employed triple quadrupole linear ion trap mass spectrometry in electrospray positive ion mode. Primary mass spectrometry scanning was utilized to confirm parent ions, while secondary mass spectrometry scanning enabled the observation of fragment ions. The cleavage characteristics and pathways of the compounds were inferred from accurate mass-to-charge ratios obtained from both primary and secondary mass spectrometry. Under soft ionization conditions, the compounds generally exhibited characteristic fragment ions of [M+H-H2O]+, [M+H-CO]+, and [M+H-H2O-CO]+. Additionally, subtle variations were observed in each compound due to differences in modifying groups. For instance, upon deacidification, the piperazine ring structure underwent breakage and rearrangement, yielding fragment ion peaks devoid of neutral molecules such as C2H5N, C3H7N, or C4H8N. Notably, compounds featuring a cyclopropyl substituent group at the N-1 position typically exhibited characteristic fragments resulting from the loss of the cyclopropyl radical (⋅C3H5). Moreover, substituents at the N-1 and C-8 positions, when linked to form a six-membered carbocyclic ring, were prone to cleavage, releasing the neutral C3H6 molecule. Quinolone antibiotics share structural similarities in their parent nuclei, leading to partially similar cleavage pathways. Nevertheless, distinct cleavage patterns emerge due to variations in functional groups. According to the difference of mass spectrometry cleavage patterns, it can provide an identification basis for the measured detection of antibiotics.

Comparative study on physical properties of bio-based PA56 fibers and wearability of their fabrics

Abstract Bio-based polyamide 56 (PA56) has significant advantages in reducing oil dependence and reducing carbon emissions. This study compared the physical properties of bio-based PA56 and conventional polyamide 66 (PA66) fibers and their fabrics. The results showed that due to the odd arrangement structure of carbon atoms in PA56 fiber molecules, its crystallinity and orientation were lower than those of PA66. At the same time, PA56 had a higher degradation rate and coloring rate due to its smaller crystallinity and higher moisture content and gave PA56 fabrics better instantaneous cooling and moisture absorption and drying properties. In addition, the molecular structure characteristics of PA56 also lead to a lower stiffness and soft feel of the fabric, which is beneficial for improving the curling phenomenon of the fabric. Overall, bio-based PA56 fibers can meet the applications in the textile and clothing field and can largely replace PA66, which is worthy of further research and utilization.

The effects of chain-length distributions on starch-related properties in waxy rices

Normal rice starch consists of amylopectin and amylose, whose relative amounts and chain-length distributions (CLDs) are major determinants of the digestibility and rheology of cooked rice, and are related to metabolic health and consumer preference. Here, the mechanism of how molecular structural features of pure amylopectin (waxy) starches affect starch properties was explored. Following debranching, chain-length distributions of seven waxy varieties were measured using size-exclusion chromatography, and parameterized using biosynthesis-based models, which involve breaking up the chain-length distribution into contributions from five enzyme sets covering overlapping ranges of chain length; structure-property correlations involving the fifth set were found to be statistically significant. Digestibility was measured in vitro, and parameters for the slower and longer digestion phase quantified using non-linear least-squares fitting. The coefficient for the significant correlation involving amylopectin fine structure for the fifth set was −0.903, while the amounts of amylopectin short and long chains were found to dominate breakdown viscosity (correlation coefficients 0.801 and − 0.911, respectively). This provides a methodology for finding or developing healthier starch in terms of lower digestion rate, while also having acceptable palatability. As rice breeders can to some extent control CLDs, this can help the development of waxy rices with improved properties.

Enhancing Knowledge Graph Embedding with Hierarchical Self-Attention and Graph Neural Network Techniques for Drug-Drug Interaction Prediction in Virtual Reality Environments

In biomedicine, the critical task is to decode Drug–Drug Interactions (DDIs) from complex biomedical texts. The scientific community employs Knowledge Graph Embedding (KGE) methods, enhanced with advanced neural network technologies, including capsule networks. However, existing methodologies primarily focus on the structural details of individual entities or relations within Biomedical Knowledge Graphs (BioKGs), overlooking the overall structural context of BioKGs, molecular structures, positional features of drug pairs, and their critical Relational Mapping Properties. To tackle the challenges identified, this study presents HSTrHouse an innovative hierarchical self-attention BioKGs embedding framework. This architecture integrates self-attention mechanisms with advanced neural network technologies, including Convolutional Neural Network (CNN) and Graph Neural Network (GNN), for enhanced computational modeling in biomedical contexts. The model bifurcates the BioKGs into entity and relation layers for structural analysis. It employs self-attention across these layers, utilizing PubMedBERT and CNN for position feature extraction, and a GNN for drug pair molecular structure analysis. Then, we connect the position and molecular structure features to integrate them into the self-attention calculation of entity and relation. After that, the output of the self-attention layer is combined with the connected vectors of the position feature and molecular structure feature to obtain the final representation vector, and finally, to model the Relational Mapping Properties (RMPs), the representation vector is embedded into the complex vector space using Householder projections to obtain the BioKGs model. The paper validates HSTrHouse’s efficacy by comparing it with advanced models on three standard BioKGs for DDIs research.

Experimental and kinetic analysis of the fluorocarbon structures and coal particles at the microscale

Understanding the dynamic wetting process between liquid droplets and coal dust particles is crucial. Compared to other substances, coal possesses a more intricate microscale molecular chemical structure, with coal's molecular chemical and physical structural characteristics being the primary microscale factors influencing its wetting properties. To enhance the wetting and permeability performance of coal dust, an analysis of the microstructural influences on the wetting process of coal dust through experiments and simulations with five different structured fluorocarbon solutions: perfluorooctane sulfonate sodium (A1), perfluoroisopropyl acrylate (A2), perfluorooctane sulfonic acid ammonium salt (A3), perfluorooctyl alcohol polyoxyethylene ether (N1), and perfluorohexyl ethanol polyoxyethylene ether (N2), was conducted. A wetting theory model (collision–adsorption–immersion) was proposed based on experiments with different concentrations and types of surface tension, and wetting experiments were conducted based on this theory model. The results indicate that smaller coal particle sizes facilitate solution penetration, with the N2 solution demonstrating the best wetting and permeation effects. Microstructural experimental analysis revealed that N2 has more polar functional group structures than the other four fluorocarbon solutions. To further investigate the forces of different structures on coal particles, a molecular dynamics model was employed, and the simulation results indicated that the interaction forces and the number of hydrogen bonds representing the adsorption capacity in the N2 system were the highest.

Nonlinear responses of biofilm bacteria to alkyl-chain length of parabens by DFT calculation

Parabens can particularly raise significant concerns regarding the disruption of microbial ecology due to their antimicrobial properties. However, the responses of biofilm bacteria to diverse parabens with different alkyl-chain length remains unclear. Here, theoretical calculations and bioinformatic analysis were performed to decipher the influence of parabens varying alkyl-chain lengths on the biofilm bacteria. Our results showed that the disturbances in bacterial community did not linearly response to the alkyl-chain length of parabens, and propylparaben (PrP), with median chain length, had more severe impact on bacterial community. Despite the fact that paraben lethality linearly increased with chain length, the PrP had a higher chemical reactions potential than parabens with shorter or longer alkyl-chain. The chemical reactions potential was critical in the nonlinear responses of bacterial community to alkyl-chain length of parabens. PrP could impose selective pressure to disturb the bacterial community, because it had a more profound contribution to deterministic assembly process. Furthermore, N-acyl-homoserine lactones was also significantly promoted under PrP exposure, confirming that PrP could affect the bacterial community by influencing the quorum-sensing system. Overall, our study reveals the nonlinear responses of bacterial communities to the alkyl-chain lengths of parabens and provides insightful perspectives for the better regulation of parabens. Environmental ImplicationParabens are recognized as emerging organic pollutants, which specially raise great concerns due to their antimicrobial properties disturbing microbial ecology. However, few study have addressed the relationship between bacterial community responses and the molecular structural features of parabens with different alkyl-chain length. This investigation revealed nonlinear responses of the bacterial community to the alkyl-chain length of parabens through DFT calculation and bioinformatic analysis and identified the critical roles of chemical reactions potential in nonlinear responses of bacterial community. Our results benefit the precise evaluation of ecological hazards posed by parabens and provide useful insights for better regulation of parabens.

Synthesis of new peptide-based macrocycles: Evaluation of their in vitro anti-proliferative activity and molecular dynamic simulation studies

A simplified synthetic protocol was adopted for the preparation of the six novel cyclo-pentapeptide candidates of the structure cyclo-(m-phthaloyl)-bis-[L-Phe -Gly/Sar]-L-Lys–X, where X = COOMe (9a,b), COOH (10a,b) and NHNH2 (11a,b). The potential cytotoxicity (IC50, μg/mL) for cyclo-pentapeptides (9a,b), (10a,b), (11a,b) and doxorubicin® as reference drugs, is assayed against eight cell lines of human cancer namely; mamillary MCF-7 and T47D, hepatic HEPG2, colon HCT116, cell line of cervix HELA, larynx HEP2, Prostate PC3, and gut Caco. Cyclo-[m-phthaloyl chloride-bis-(L-phalanine-sar)-L-Lys]-COOH 10b was proved as cytotoxically more potent than doxorubicin against most of the screened cell lines. It can be inferred that the proposed molecular structural characteristics associated with cytotoxicity in these m-phthaloyl-bridged cyclopeptides appear noteworthy and hold promise as potential candidates for further exploration as novel anticancer agents. Molecular dynamics simulation studies of 10b into the binding site of Human Epidermal Growth Factor Receptor tyrosine kinase exhibited the highest MM-GBSA value (-16.59 kcal/mol). KEY WORDS: Anticancer, Cytotoxicity, Cyclopeptides, Cyclopeptides, m-phthaloyl-bis-peptides Bull. Chem. Soc. Ethiop. 2024, 38(4), 1129-1144. DOI: https://dx.doi.org/10.4314/bcse.v38i4.25

Characterization and differentiation of dissolved organic matter in leachate derived from an old Japanese landfill site through Orbitrap mass spectrometry

Elucidating the characteristics of dissolved organic matter (DOM) is crucial to assessing its impact on the bioavailability and mobility of pollutants in landfill leachate. This study reports a comprehensive 5–month investigation into the characteristics of DOM in leachate from an old Japanese landfill, collected at six different sampling points. The molecular composition, chemical properties, and structural characteristics of DOM were assessed using Orbitrap mass spectrometry and spectral analysis. The leachate DOM mainly consisted of CHO-containing molecules (58.5–88.9%), low-oxygen unsaturated phenolic compounds (40.5–54.0%), and aliphatic compounds (19.4–47.3%), with slight variation among sampling points. A significant portion of the nominal oxidation state of carbon was in the reduced zone (76.2–95.4%). The results underscore the distinct molecular composition of DOM in mature Japanese landfill leachate compared to young and mature leachates from other countries. Two of six sampling points, with notable differences in molecular characteristics, were compared and elucidated. The composition of landfill waste, rather than landfill age, was the main factor affecting the characteristics and differentiation of leachate DOM.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

A comparative study of molecular structure, rheology, and thermal characteristics of deacetylated konjac glucomannan-treated gluten during frozen storage

Konjac glucomannan is extensively employed to enhance the quality of wheat-based products. Despite this, the utilization of natural KGM in the food industry is restricted due to its inadequate mechanical attributes, heightened susceptibility to humidity, and unappealing taste profile. In this study, the influence of deacetylated konjac glucomannan (DKGM) on the structure, rheology, and thermal characteristics of gluten during frozen storage was explored. Furthermore, the mechanism outlining the action between gluten and DKGM was analyzed. The secondary structure results showed that DKGM promoted the formation of α-helices and β-sheets in gluten. Scanning electron microscopy analysis confirmed that DKGM made the gluten network more stable. Moreover, DKGM provided a significant improvement in the rheological and thermal properties of frozen gluten. This is attributes to the fact that DKGM alters the hydrophobic residues in frozen gluten. Overall, this work indicates that the molecular structure and thermal properties of gluten are optimally substantial improvements at a substitution content of 1.5% D3 (deacetylation degree of 95%) in the dough. These findings provide a reference value for the application of DKGM in frozen wheat-based foods.

Molecular Structural Features that Determine the Neurotropic Activity of Thiamine Derivatives

Molecular Structural Features that Determine the Neurotropic Activity of Thiamine Derivatives

Construction of a food waste biochar model and determination of contaminant adsorption sites: Combining experimental characterisation with quantum chemical calculations

Increasing urbanization has made it a serious challenge to deal with food waste (FW) from people and wastewater with pollutants from industry. Biochar from FW pyrolysis(FWB) can be used as an adsorbent to adsorb pollutants from wastewater, but its molecular structure is still unknown, limiting the understanding of its adsorption mechanism and its application in industry. In this paper, a series of combined methods of experimental characterization and quantum chemical calculations, such as solid-state 13C nuclear magnetic resonance spectroscopy (13C NMR), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS) were used to investigate and successfully construct a model of the molecular structural features of FWB under the optimal contaminant adsorption preparation conditions. Based on the electrostatic potential distribution and adsorption experiments, the adsorption sites of the pollutants were elucidated. The results showed that the FWB prepared at different temperatures, 450 °C had a variety of oxygen-containing functional groups and polarity favorable for adsorption. In the molecular structure of FWB with the molecular formula C60H49N7O8, oxygen is present in the form of carboxyl, carbonyl, ether, alcohol hydroxyl, and phenol hydroxyl groups with numbers 1, 1, 1, 1, 2, 1, 2. Nitrogen is present in the form of pyridinyl nitrogen, pyrrolidinyl nitrogen, and primary amines with numbers 1, 1, and 5, respectively. In addition, nitrogen-containing and oxygen-containing functional groups are possible adsorption sites for pollutants on FWB, and alcohol hydroxyl and amino groups can bind better to Cu2+, which provides scientific guidance for improving the adsorption performance of biochar. This provides a solid foundation for the study of the microscopic adsorption mechanism of FWB and a method for the modeling of biochar from other biomasses.

Pyrolysis mechanism and evolved gas analysis of a promising energetic carbamate-functionalized microcrystalline cellulose nitrate

The present study aims to elucidate the decomposition mechanism and gas evolution characteristics of a promising energy-rich carbamated microcrystalline cellulose nitrate (M3CN). The molecular structure and morphological characteristics of starting microcrystalline cellulose carbamate (MCCC) and its nitrated derivative were examined using FTIR and SEM techniques. Thermal analysis using TGA and DSC revealed distinct decomposition behaviors for MCCC and M3CN. MCCC exhibited endothermic decomposition linked to the degradation of the cellulosic structure. In contrast, an exothermic decomposition event was observed for M3CN, attributed to the cleavage of energetic groups within the nitrated cellulosic chains. Furthermore, the hyphenated TG-FTIR analysis confirmed that the primary gaseous products emitted during the pyrolysis of M3CN included NO, N2O, NO2, CO2, H2O, CH4, HCHO, HCN, and CHNO. The findings of this study enhance our understanding of the pyrolysis mechanism in cellulose-based energetic materials, providing a significant reference for forthcoming research and explorations in this field.