Search Results (12,729)

Intratumoral injections of macromolecules, such as biologics and immunotherapeutics, show promise in overcoming dose-limiting side effects associated with systemic injections and improve treatment efficacy. However, the retention of injectates in the tumor microenvironment is a major underappreciated challenge. High interstitial pressures and dense tumor architectures create shear forces that rapidly expel low-viscosity solutions post-injection. Injectable hydrogels may address these concerns by providing a viscoelastic delivery vehicle that shields loaded therapies from rapid expulsion from the tumor. A chitosan–glycerol hydrogel was thus developed and characterized with the goal of improving the injection retention of loaded therapeutics. The gelation parameters and mechanical properties of the hydrogel were explored to reveal a shear-thinning gel that is injectable through a 27-gauge needle. Biocompatibility studies demonstrated that the chitosan–glycerol hydrogel was nontoxic. Retention studies revealed significant improvements in the retention of model therapeutics when formulated with the chitosan–glycerol hydrogel compared to less-viscous solutions. Finally, release studies showed that there was a sustained release of model therapeutics of various molecular sizes from the hydrogel. Overall, the chitosan–glycerol hydrogel demonstrated injectability, enhanced retention, biocompatibility, and sustained release of macromolecules, indicating its potential for future clinical use in intratumoral macromolecule delivery. Full article

(1) Background: Polyploid fish are highly important in increasing fish production, improving fish quality, and breeding new varieties. The loach (Misgurnus anguillicaudatus), as a naturally polyploid fish, serves as an ideal biological model for investigating the mechanisms of chromosome doubling; (2) Methods: In this study, tetraploidization in diploid loach was induced by heat shock treatment, and, for the first time, the role of the key cell cycle gene cdk1 (cyclin-dependent kinase 1) in chromosome doubling was investigated; (3) Results: The experimental results show that when eggs are fertilized for 20 min and then subjected to a 4 min heat shock treatment at 39–40 °C, this represents the optimal induction condition, resulting in a tetraploid rate of 44%. Meanwhile, the results of the cdk1 knockout model (2n cdk1^−/−) constructed using CRISPR/Cas9 showed that the absence of cdk1 significantly increased the chromosome doubling efficiency of the loach. The qPCR analysis revealed that knockout of cdk1 significantly upregulated cyclin genes (ccnb3,ccnc, and ccne1), while inhibiting expression of the separase gene espl1 (p < 0.05); (4) Conclusions: During chromosome doubling in diploid loaches induced by heat shock, knocking out the cdk1 gene can increase the tetraploid induction rate. This effect may occur through downregulation of the espl1 gene. This study offers novel insights into optimizing the induced breeding technology of polyploid fish and deciphering its molecular mechanism, while highlighting the potential application of integrating gene editing with physical induction. Full article

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

Background/Objectives: Most snakebite incidents in Latin America are caused by species of the Bothrops genus. Their venom induces severe local effects, against which antivenom therapy has limited efficacy. Metabolites derived from Coffea arabica have demonstrated anti-inflammatory and anticoagulant properties, suggesting their potential as therapeutic agents to inhibit the local effects induced by B. asper venom. Methods: Three enzymatic assays were performed: inhibition of the procoagulant and amidolytic activities of snake venom serine proteinases (SVSPs); inhibition of the proteolytic activity of snake venom metalloproteinases (SVMPs); and inhibition of the catalytic activity of snake venom phospholipases A₂ (PLA_2s). Additionally, molecular docking studies were conducted to propose potential inhibitory mechanisms of the metabolites chlorogenic acid, caffeine, and caffeic acid. Results: Green and roasted coffee extracts partially inhibited the enzymatic activity of SVSPs and SVMPs. Notably, the green coffee extract, at a 1:20 ratio, effectively inhibited PLA₂ activity. Among the individual metabolites tested, partial inhibition of SVSP and PLA₂ activities was observed, whereas no significant inhibition of SVMP proteolytic activity was detected. Chlorogenic acid was the most effective metabolite, significantly prolonging plasma coagulation time and achieving up to 82% inhibition at a concentration of 62.5 μM. Molecular docking analysis revealed interactions between chlorogenic acid and key active site residues of SVSP and PLA₂ enzymes from B. asper venom. Conclusions: The roasted coffee extract demonstrated the highest inhibitory effect on venom toxins, potentially due to the formation of bioactive compounds during the Maillard reaction. Molecular modeling suggests that the tested inhibitors may bind to and occupy the substrate-binding clefts of the target enzymes. These findings support further in vivo research to explore the use of plant-derived polyphenols as adjuvant therapies in the treatment of snakebite envenoming. Full article

Background: Pain is a complex phenomenon characterized by unpleasant experiences with profound heterogeneity influenced by biological, psychological, and social factors. According to the National Health Interview Survey, 50.2 million U.S. adults (20.5%) experience pain on most days, with the annual cost of prescription medication for pain reaching approximately USD 17.8 billion. Theranostic pain nanomedicine therefore emerges as an attractive analgesic strategy with the potential for increased efficacy, reduced side-effects, and treatment personalization. Theranostic nanomedicine combines drug delivery and diagnostic features, allowing for real-time monitoring of analgesic efficacy in vivo using molecular imaging. However, clinical translation of these nanomedicines are challenging due to complex manufacturing methodologies, lack of standardized quality control, and potentially high costs. Quality by Design (QbD) can navigate these challenges and lead to the development of an optimal pain nanomedicine. Our lab previously reported a macrophage-targeted perfluorocarbon nanoemulsion (PFC NE) that demonstrated analgesic efficacy across multiple rodent pain models in both sexes. Here, we report PFC-free, biphasic nanoemulsions formulated with a biocompatible and non-immunogenic plant-based coconut oil loaded with a COX-2 inhibitor and a clinical-grade, indocyanine green (ICG) near-infrared fluorescent (NIRF) dye for parenteral theranostic analgesic nanomedicine. Methods: Critical process parameters and material attributes were identified through the FMECA (Failure, Modes, Effects, and Criticality Analysis) method and optimized using a 3 × 2 full-factorial design of experiments. We investigated the impact of the oil-to-surfactant ratio (w/w) with three different surfactant systems on the colloidal properties of NE. Small-scale (100 mL) batches were manufactured using sonication and microfluidization, and the final formulation was scaled up to 500 mL with microfluidization. The colloidal stability of NE was assessed using dynamic light scattering (DLS) and drug quantification was conducted through reverse-phase HPLC. An in vitro drug release study was conducted using the dialysis bag method, accompanied by HPLC quantification. The formulation was further evaluated for cell viability, cellular uptake, and COX-2 inhibition in the RAW 264.7 macrophage cell line. Results: Nanoemulsion droplet size increased with a higher oil-to-surfactant ratio (w/w) but was no significant impact by the type of surfactant system used. Thermal cycling and serum stability studies confirmed NE colloidal stability upon exposure to high and low temperatures and biological fluids. We also demonstrated the necessity of a solubilizer for long-term fluorescence stability of ICG. The nanoemulsion showed no cellular toxicity and effectively inhibited PGE2 in activated macrophages. Conclusions: To our knowledge, this is the first instance of a celecoxib-loaded theranostic platform developed using a plant-derived hydrocarbon oil, applying the QbD approach that demonstrated COX-2 inhibition. Full article

The removal of high-concentration m-cresol from industrial wastewater remains a significant challenge due to its toxicity and persistence. In this study, a commercially available functionalized resin with a high BET surface area (1439 m² g⁻¹) and hierarchical pore structure was employed for the adsorption of pure m-cresol at an initial concentration of 20 g L⁻¹, representative of coal-based industrial effluents. Comprehensive characterization confirmed the presence of oxygen-rich functional groups, amorphous polymeric structure, and uniform surface morphology conducive to adsorption. Batch experiments were conducted to evaluate the effects of resin dosage, contact time, temperature, and equilibrium concentration. Under optimized conditions (0.15 g resin, 60 °C), a maximum adsorption capacity of 556.3 mg g⁻¹ and removal efficiency of 71% were achieved. Kinetic analysis revealed that the pseudo-second-order model best described the adsorption process (R² > 0.99). Isotherm data fit the Langmuir model most closely (R² = 0.9953), yielding a monolayer capacity of 833.3 mg g⁻¹. Thermodynamic analysis showed that adsorption was spontaneous (ΔG° < 0), endothermic (ΔH° = 7.553 kJ mol⁻¹), and accompanied by increased entropy (ΔS° = 29.90 J mol⁻¹ K⁻¹). The good agreement with the PSO model is indicative of chemisorption, as supported by other lines of evidence, including thermodynamic parameters (e.g., positive ΔH° and ΔS°), surface functional group characteristics, and molecular interactions. The adsorption mechanism was elucidated through comprehensive modeling of adsorption kinetics, isotherms, and thermodynamics, combined with detailed physicochemical characterization of the resin prior to adsorption, reinforcing the mechanistic understanding of m-cresol–resin interactions. Full article

This study investigates the self-assembly and host–guest complexation behaviour of novel resveratrol-based lipophenols (LipoResv)—resveratrol-4′-linoleate (Resv-4′-LA) and resveratrol-4′-docosahexaenoate (Resv-4′-DHA)—with hydroxypropyl-β-cyclodextrins (HP-β-CDs). These amphiphilic molecules display surfactant-like properties, forming micellar aggregates in aqueous media. Fluorescence spectroscopy was used to determine the critical micelle concentration (CMC), revealing that LipoResv exhibit significantly lower CMC values than their free fatty acids, indicating higher hydrophobicity. The formation of inclusion complexes with HP-β-CDs was evaluated based on changes in CMC values and further confirmed by dynamic light scattering (DLS) and molecular modelling analyses. Resv-4′-LA formed 1:1 complexes (Kc = 720 M⁻¹), while Resv-4′-DHA demonstrated a 1:2 stoichiometry with lower affinity constants (K₁ = 17 M⁻¹, K₂ = 0.18 M⁻¹). Environmental parameters (pH, temperature, and ionic strength) significantly modulated CMC and binding constants. Computational docking and molecular dynamics simulations supported the experimental findings by revealing the key structural determinants of the host–guest affinity and micelle stabilization. Ligand efficiency (LE) analysis further aligned with the experimental data, favouring the unmodified fatty acids. These results highlight the versatile encapsulation capacity of HP-β-CDs for bioactive amphiphile molecules and support their potential applications in drug delivery and functional food systems. Full article

Environmental DNA (eDNA) analysis has emerged as a transformative tool in environmental monitoring, enabling non-invasive detection of species and microbial communities across diverse ecosystems. This study systematically reviews the role of bioinformation technology in eDNA analysis, focusing on methodologies and applications across air, soil, groundwater, sediment, and aquatic environments. Advances in molecular biology, high-throughput sequencing, bioinformatics tools, and field-deployable detection systems have significantly improved eDNA detection sensitivity, allowing for early identification of invasive species, monitoring ecosystem health, and tracking pollutant degradation processes. Airborne eDNA monitoring has demonstrated potential for assessing microbial shifts due to air pollution and tracking pathogen transmission. In terrestrial environments, eDNA facilitates soil and groundwater pollution assessments and enhances understanding of biodegradation processes. In aquatic ecosystems, eDNA serves as a powerful tool for biodiversity assessment, invasive species monitoring, and wastewater-based epidemiology. Despite its growing applicability, challenges remain, including DNA degradation, contamination risks, and standardization of sampling protocols. Future research should focus on integrating eDNA data with remote sensing, machine learning, and ecological modeling to enhance predictive environmental monitoring frameworks. As technological advancements continue, eDNA-based approaches are poised to revolutionize environmental assessment, conservation strategies, and public health surveillance. Full article

The worldwide increase in antifungal resistance, particularly in Candida sp., requires the exploration of novel therapeutic agents. Natural compounds have been a rich source of antimicrobial molecules, where peptides constitute the class of the most bioactive components. Therefore, this study looks into the target-specific binding efficacy of insect-derived antifungal peptides (n = 37) as possible alternatives to traditional antifungal treatments. Using computational methods, namely the HPEPDOCK and HDOCK platforms, molecular docking was performed to evaluate the interactions between selected key fungal targets, lanosterol 14-demethylase, or LDM (PDB ID: 5V5Z), secreted aspartic proteinase-5, or Sap-5 (PDB ID: 2QZX), N-myristoyl transferase, or NMT (PDB ID: 1NMT), and dihydrofolate reductase, or DHFR, of C. albicans. The three-dimensional peptide structure was modelled through the PEP-FOLD 3.5 tool. Further, we predicted the physicochemical properties of these peptides through the ProtParam and PEPTIDE 2.0 tools to assess their drug-likeness and potential for therapeutic applications. In silico results show that Blap-6 from Blaps rhynchopeter and Gomesin from Acanthoscurria gomesiana have the most antifungal potential against all four targeted proteins in Candida sp. Additionally, a molecular dynamics simulation study of LDM-Blap-6 was carried out at 100 nanoseconds. The overall predictions showed that both have strong binding abilities and are good candidates for drug development. In in vitro studies, Gomesin achieved complete biofilm eradication in three out of four Candida species, while Blap-6 showed moderate but consistent reduction across all species. C. tropicalis demonstrated relative resistance to complete eradication by both peptides. The present study provides evidence to support the antifungal activity of certain insect peptides, with potential to be used as alternative drugs or as a template for a new synthetic or modified peptide in pursuit of effective therapies against Candida spp. Full article

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

Protein–polyelectrolyte nanostructures assembled via electrostatic interactions offer versatile applications in biomedicine, tissue engineering, and food science. However, several open questions remain regarding their intermolecular interactions and the influence of external conditions—such as temperature and pH—on their assembly, stability, and responsiveness. This study explores the formation and stability of networks between poly(acrylic acid) (PAA) and lysozyme (LYZ) at the nanoscale upon thermal treatment, using a combination of experimental and simulation measures. Experimental techniques of static and dynamic light scattering (SLS and DLS), Fourier transform infrared spectroscopy (FTIR), and circular dichroism (CD) are combined with all-atom molecular dynamics simulations. Model systems consisting of multiple PAA and LYZ molecules explore collective assembly and complexation in aqueous solution. Experimental results indicate that electrostatic complexation occurs between PAA and LYZ at pH values below LYZ’s isoelectric point. This leads to the formation of nanoparticles (NPs) with radii ranging from 100 to 200 nm, most pronounced at a PAA/LYZ mass ratio of 0.1. These complexes disassemble at pH 12, where both LYZ and PAA are negatively charged. However, when complexes are thermally treated (TT), they remain stable, which is consistent with earlier findings. Atomistic simulations demonstrate that thermal treatment induces partially reversible structural changes, revealing key microscopic features involved in the stabilization of the formed network. Although electrostatic interactions dominate under all pH and temperature conditions, thermally induced conformational changes reorganize the binding pattern, resulting in an increased number of contacts between LYZ and PAA upon thermal treatment. The altered hydration associated with conformational rearrangements emerges as a key contributor to the stability of the thermally treated complexes, particularly under conditions of strong electrostatic repulsion at pH 12. Moreover, enhanced polymer chain associations within the network are observed, which play a crucial role in complex stabilization. These insights contribute to the rational design of protein–polyelectrolyte materials, revealing the origins of association under thermally induced structural rearrangements. Full article

This study measured eight key phenotypic traits across 259 fresh maize inbred lines, including plant height and spike length. A total of 82 single nucleotide polymorphisms (SNPs) significantly associated with these phenotypes were identified by applying a mixed linear model to calculate the best linear unbiased prediction (BLUP) values and integrating genome-wide genotypic data through genome-wide association analysis (GWAS). A further analysis of significant SNPs contributed to the identification of 63 candidate genes with functional annotations. Notably, 11 major candidate genes were identified from multi-trait association loci, all of which exhibited highly significant P-values (<0.0001) and explained between 7.21% and 12.78% of phenotypic variation. These 11 genes, located on chromosomes 1, 3, 4, 5, 6, and 9, were functionally involved in signaling, metabolic regulation, structural maintenance, and stress response, and are likely to play crucial roles in the growth and physiological processes of fresh maize inbred lines. The functional genes identified in this study have significant implications for the development of molecular markers, the optimization of breeding strategies, and the enhancement of quality in fresh maize. Full article

Schizophrenia is a challenging multifactorial neuropsychiatric disease that involves interactions between genetic susceptibility and environmental insults. Increasing evidence implicates viral infections as significant environmental contributors, particularly during sensitive neurodevelopmental periods. This review synthesises current findings on the viral hypothesis of schizophrenia, encompassing a wide array of neurotropic viruses, including influenza viruses, herpesviruses (HSV-1 and 2, CMV, VZV, EBV, HHV-6 and 8), hepatitis B and C viruses, HIV, HERVs, HTLV, Zika virus, BoDV, coronaviruses (including SARS-CoV-2), and others. These pathogens can contribute to schizophrenia through mechanisms such as direct microinvasion, persistent central nervous system infection, immune-mediated neuroinflammation, molecular mimicry, and the disturbance of the blood–brain barrier. Prenatal exposure to viral infections can trigger maternal immune activation, resulting in cytokine-mediated alterations in the neurological development of the foetus that persist into adulthood. Genetic studies highlight the role of immune-related loci, including major histocompatibility complex polymorphisms, in modulating susceptibility to infection and neurodevelopmental outcomes. Clinical data also support the “mild encephalitis” hypothesis, suggesting that a subset of schizophrenia cases involve low-grade chronic neuroinflammation. Although antipsychotics have some immunomodulatory effects, adjunctive anti-inflammatory therapies show promise, particularly in treatment-resistant cases. Despite compelling associations, pathogen-specific links remain inconsistent, emphasising the need for longitudinal studies and integrative approaches such as viromics to unravel causal relationships. This review supports a “multi-hit” model in which viral infections interfere with hereditary and immunological susceptibilities, enhancing schizophrenia risk. Elucidating these virus–immune–brain interactions may facilitate the discovery of biomarkers, targeted prevention, and novel therapeutic strategies for schizophrenia. Full article

Deep marine shale is the primary carrier of shale gas resources in Southwestern China. Because the occurrence and gas content of methane vary with burial conditions, understanding the microscopic mechanism of methane occurrence in deep marine shale is critical for effective shale gas exploitation. The temperature and pressure conditions in deep shale exceed the operating limits of experimental equipment; thus, few studies have discussed the microscopic occurrence mechanism of shale gas in deep marine shale. This study applies molecular simulation technology to reveal the methane’s microscopic occurrence mechanism, particularly the main controlling factor of adsorbed methane in deep marine shale. Two types of simulation models are also proposed. The Grand Canonical Monte Carlo (GCMC) method is used to simulate the adsorption behavior of methane molecules in these two models. The results indicate that the isosteric adsorption heat of methane in both models is below 42 kJ/mol, suggesting that methane adsorption in deep shale is physical adsorption. Adsorbed methane concentrates on the pore wall surface and forms a double-layer adsorption. Furthermore, adsorbed methane can transition to single-layer adsorption if the pore size is less than 1.6 nm. The total adsorption capacity increases with rising pressure, although the growth rate decreases. Excess adsorption capacity is highly sensitive to pressure and can become negative at high pressures. Methane adsorption capacity is determined by pore size and adsorption potential, while accommodation space and adsorption potential are influenced by pore size and mineral type. Under deep marine shale reservoir burial conditions, with burial depth deepening, the effect of temperature on shale gas occurrence is weaker than pressure. Higher temperatures inhibit shale gas occurrence, and high pressure enhances shale gas preservation. Smaller pores facilitate the occurrence of adsorbed methane, and larger pores have larger total methane adsorption capacity. Deep marine shale with high formation pressure and high clay mineral content is conducive to the microscopic accumulation of shale gas in deep marine shale reservoirs. This study discusses the microscopic occurrence state of deep marine shale gas and provides a reference for the exploration and development of deep shale gas. Full article