Layered Double Hydroxide Derivatives for Polyolefin Upcycling.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

AMIGO2 is a pivotal therapeutic target related to M2 polarization of macrophages in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a common kind of lethal cancer, with low early diagnostic rate and poor prognosis. In this study, we identified and verified the AMIGO2 with significant diagnostic and prognostic value in PDAC through LASSO regression combined with multiple machines learning methods, including RVM-RFE and Random Forest in TCGA and GEO datasets. The relevance between the expression of AMIGO2 and M2 polarization of macrophages was identified through pancancer, normal tissue, and cell lines data in TCGA, GTEx and CCLE datasets. The relevance between AMIGO2 and M2 polarization was then further identified in our local PDAC cohort. Finally, the role of AMIGO2 as cancer promoter and pivotal factor enrolled in M2 polarization was verified through siRNA transfection and M2 macrophages induction. These findings could facilitate diagnosis and treatment of PDAC. In addition, further research was deemed necessary on the deep mechanism between AMIGO2 and M2 polarization of macrophages in PDAC.

MICDnet: Multimodal information processing networks for Crohn's disease diagnosis.

Crohn's disease (CD) is a chronic inflammatory disease with increasing incidence worldwide and unclear etiology. Its clinical manifestations vary depending on location, extent, and severity of the lesions. In order to diagnose Crohn's disease, medical professionals need to comprehensively analyze patients' multimodal examination data, which includes medical imaging such as colonoscopy, pathological, and text information from clinical records. The processes of multimodal data analysis require collaboration among medical professionals from different departments, which wastes a lot of time and human resources. Therefore, a multimodal medical assisted diagnosis system for Crohn's disease is particularly significant. Existing network frameworks find it hard to effectively capture multimodal patient data for diagnosis, and multimodal data for Crohn's disease is currently lacking. In addition,a combination of data from patients with similar symptoms could serve as an effective reference for disease diagnosis. Thus, we propose a multimodal information diagnosis network (MICDnet) to learn CD feature representations by integrating colonoscopy, pathology images and clinical texts. Specifically, MICDnet first preprocesses each modality data, then uses encoders to extract image and text features separately. After that, multimodal feature fusion is performed. Finally, CD classification and diagnosis are conducted based on the fused features. Under the authorization, we build a dataset of 136 hospitalized inspectors, with colonoscopy images of seven areas, pathology images, and clinical record text for each individual. Training MICDnet on this dataset shows that multimodal diagnosis can improve the diagnostic accuracy of CD, and the diagnostic performance of MICDnet is superior to other models.

Confinement Effects in Carbonized ZIF-Confined Hollow PtCo Nanospheres Enable the Methanol Oxidation Reaction.

Confinement effects in highly porous nanostructures can effectively adjust the selectivity and kinetics of electrochemical reactions, which can boost the methanol oxidation reaction (MOR). In this work, carbonized ZIF-8-confined hollow PtCo nanospheres (PtCo@carbonized ZIF-8) were fabricated using a facile strategy. A monodisperse confined region was successfully prepared, and the dispersion of the PtCo nanoparticles (NPs) could be precisely regulated, allowing for the effective tuning of the confined region. Thus, the precise regulation of the catalytic reaction was achieved. Importantly, hollow PtCo NPs were prepared using a method based on the Kirkendall effect, and their forming mechanism was systematically investigated. Because of the confinement effects of carbonized zeolitic imidazolate framework-8 (ZIF-8), the crystal and electronic structures of the PtCo NPs were able to be effectively tuned. Our electrochemical results show that PtCo@carbonized ZIF-8 composites manifest a higher mass activity (1.4 A mgPt-1) and better stability compared to commercial Pt/C.

Iodotrimethylsilane as a Reactive Ligand for Surface Etching and Passivation of Perovskite Nanocrystals toward Efficient Pure-red to Deep-red LEDs.

Resurfacing perovskite nanocrystals (NCs) with tight-binding and conductive ligands to resolve the dynamic ligands-surface interaction is the fundamental issue for their applications in perovskite light-emitting diodes (PeLEDs). Although various types of surface ligands have been proposed, these ligands either exhibit weak Lewis acid/base interactions or need high polar solvents for dissolution and passivation, resulting in a compromise in the efficiency and stability of PeLEDs. Herein, we report a chemically reactive agent (Iodotrimethylsilane, TMIS) to address the trade-off among conductivity, solubility and passivation using all-inorganic CsPbI3 NCs. The liquid TMIS ensures good solubility in non-polar solvents and reacts with oleate ligands and produces in situ HI for surface etching and passivation, enabling strong-binding ligands on the NCs surface. We report, as a result, red PeLEDs with an external quantum efficiency (EQE) of ≈23 %, which is 11.2-fold higher than the control, and is among the highest CsPbI3 PeLEDs. We further demonstrate the universality of this ligand strategy in the pure bromide system (CsPbBr3 ), and report EQE of ≈20 % at 640, 652, and 664 nm. This represents the first demonstration of a chemically reactive ligand strategy that applies to different systems and works effectively in red PeLEDs spanning emission from pure-red to deep-red.

The advantages of penehyclidine hydrochloride over atropine in acute organophosphorus pesticide poisoning: A meta-analysis.

Background: Penehyclidine hydrochloride (PHC) has been used for many years as an anticholinergic drug for the treatment of acute organophosphorus pesticide poisoning (AOPP). The purpose of this meta-analysis was to explore whether PHC has advantages over atropine in the use of anticholinergic drugs in AOPP. Methods: We searched Scopus, Embase, Cochrane, PubMed, ProQuest, Ovid, Web of Science, China Science and Technology Journal Database (VIP), Duxiu, Chinese Biomedical literature (CBM), WanFang, and Chinese National Knowledge Infrastructure (CNKI), from inception to March 2022. After all qualified randomized controlled trials (RCTs) were included, we conducted quality evaluation, data extraction, and statistical analysis. Statistics using risk ratios (RR), weighted mean difference (WMD), and standard mean difference (SMD). Results: Our meta-analysis included 20,797 subjects from 240 studies across 242 different hospitals in China. Compared with the atropine group, the PHC group showed decreased mortality rate (RR=0.20, 95% confidence intervals [CI]: 0.16-0.25, P <0.001), hospitalization time (WMD=-3.89, 95% CI: -4.37 to -3.41, P <0.001), overall incidence rate of complications (RR=0.35, 95% CI: 0.28-0.43, P <0.001), overall incidence of adverse reactions (RR=0.19, 95% CI: 0.17-0.22, P <0.001), total symptom disappearance time (SMD=-2.13, 95% CI: -2.35 to -1.90, P <0.001), time for cholinesterase activity to return to normal value 50-60% (SMD=-1.87, 95% CI: -2.03 to -1.70, P <0.001), coma time (WMD=-5.57, 95% CI: -7.20 to -3.95, P <0.001), and mechanical ventilation time (WMD=-2.16, 95% CI: -2.79 to -1.53, P <0.001). Conclusion: PHC has several advantages over atropine as an anticholinergic drug in AOPP.

6084 Cases of Adult Tetanus from China: A Literature Analysis.

Objective: To describe the clinical characteristics, treatments, and outcomes of tetanus and determine the most appropriate focus for tetanus prevention and treatment to reduce morbidity and mortality in China. Methods: Four databases, including the Chinese Bio-Medical Literature Database, Chinese National Knowledge Infrastructure, Chinese Scientific Journal Database, and Wan-fang Data, were searched from 1 January, 2000 to 30 October, 2022. Results: In total, 151 articles including 6084 tetanus patients met the inclusion criteria. Additionally, 5925 patients had their gender recorded in detail, among which 66.67% (3950/5925) were male, and 33.33% (1975/ 5925) were female. The average age in the detailed records was reported in 4773 cases, with an overall average age of 46.69. The number of patients' places of residence was 580. Those from rural areas comprised the highest percentage with 88.62% (514 / 580). The causes of injury were recorded in 1592 cases in total; injuries caused by metals, wood, and wooden spikes accounted for the highest percentage with 54.52% (868/1592). Patient outcomes were recorded in 4305 cases, with a mortality of 9.34% (402/4305). The leading causes of death included treatment terminated by family members, asphyxia due to persistent spasms, respiratory failure, and autonomic dysfunction, family automatic abandonment and asphyxia accounted for the highest percentage, both 24.00% (54/225). Conclusion: The overall success rate of tetanus treatment in China has dramatically improved, but the prevention and control of non-neonatal tetanus is still challenging. Focus should be placed on the prevention of adult tetanus and standardizing the use of sedative and spasmolytic drugs. Additionally, medical professionals should popularize tetanus prevention and treatment knowledge among the people and strengthen training in grass-roots hospitals.

SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation.

Generation of representative conformations for small molecules is a fundamental task in cheminformatics and computer-aided drug discovery, but capturing the complex distribution of conformations that contains multiple low energy minima is still a great challenge. Deep generative modeling, aiming to learn complex data distributions, is a promising approach to tackle the conformation generation problem. Here, inspired by stochastic dynamics and recent advances in generative modeling, we developed SDEGen, a novel conformation generation model based on stochastic differential equations. Compared with existing conformation generation methods, it enjoys the following advantages: (1) high model capacity to capture multimodal conformation distribution, thereby searching for multiple low-energy conformations of a molecule quickly, (2) higher conformation generation efficiency, almost ten times faster than the state-of-the-art score-based model, ConfGF, and (3) a clear physical interpretation to learn how a molecule evolves in a stochastic dynamics system starting from noise and eventually relaxing to the conformation that falls in low energy minima. Extensive experiments demonstrate that SDEGen has surpassed existing methods in different tasks for conformation generation, interatomic distance distribution prediction, and thermodynamic property estimation, showing great potential for real-world applications.

Environmental Determinants for Snail Density in Dongting Lake Region: An Ecological Study Incorporating Spatial Regression.

This study explored the environmental determinants of different months on snail density measured in April at different types of snail habitats (marshlands, inner embankments, and hills) by considering spatial effects. Data were gathered from surveys on snails that were conducted in Hunan Province in April 2016, and information was collected on environmental variables. To investigate the environmental factors influencing snail density in various types of snail habitats, the ordinary least square model, spatial lag model, and spatial error model were all used. The environmental determinants for snail density showed different effects in the three types of snail habitats. In marshlands, snail density measured in April was associated positively with the normalized difference vegetation index (NDVI) and was associated negatively with flooding duration and annual hours of sunshine. Extreme temperatures correlated strongly to snail density measured in April (P < 0.05). In areas inside embankments, snail density measured in April increased with a decreased distance between snail habitat and the nearest river (P < 0.05). In hills, extreme heat, annual hours of sunshine, NDVI in September, and annual average land surface temperature (LST) were associated negatively with snail density measured in April, whereas index of moisture (IM) was associated positively with snail density measured in April (P < 0.05). The effects of LST and hours of sunshine on snail density measured in April varied with months of the year in the three different types of snail habitats (P < 0.05). Our study might provide a theoretical foundation for preventing snail transmission and subsequent spread of schistosomiasis.

Spatial Analysis of Schistosomiasis in Hunan and Jiangxi Provinces in the People's Republic of China.

Understanding the spatial distribution of schistosome infection is critical for tailoring preventive measures to control and eliminate schistosomiasis. This study used spatial analysis to determine risk factors that may impact Schistosoma japonicum infection and predict risk in Hunan and Jiangxi Provinces in the People's Republic of China. The study employed survey data collected in Hunan and Jiangxi in 2016. Independent variable data were obtained from publicly available sources. Bayesian-based geostatistics was used to build models with covariate fixed effects and spatial random effects to identify factors associated with the spatial distribution of infection. Prevalence of schistosomiasis was higher in Hunan (12.8%) than Jiangxi (2.6%). Spatial distribution of schistosomiasis varied at pixel level (0.1 × 0.1 km), and was significantly associated with distance to nearest waterbody (km, ß = -1.158; 95% credible interval [CrI]: -2.104, -0.116) in Hunan and temperature (°C, ß = -4.359; 95% CrI: -9.641, -0.055) in Jiangxi. The spatial distribution of schistosomiasis in Hunan and Jiangxi varied substantially and was significantly associated with distance to nearest waterbody. Prevalence of schistosomiasis decreased with increasing distance to nearest waterbody in Hunan, indicating that schistosomiasis control should target individuals in close proximity to open water sources as they are at highest risk of infection.

Identification of biomarkers and the mechanisms of multiple trauma complicated with sepsis using metabolomics.

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

Mutation of rpoS is Beneficial for Suppressing Organic Acid Secretion During 1,3-Propandiol Biosynthesis in Klebsiella pneumoniae.

In this study, to reduce the formation of organic acid during 1,3-propanediol biosynthesis in Klebsiella pneumoniae, a method combining UV mutagenesis and high-throughput screening with pH color plates was employed to obtain K. pneumoniae mutants. When compared with the parent strain, the total organic acid formation by the mutant decreased, whereas 1,3-propanediol biosynthesis increased after 24 h anaerobic shake flask culture. Subsequently, genetic changes in the mutant were analyzed by whole-genome sequencing and verified by signal gene deletion. Mutation of the rpoS gene was confirmed to contribute to the regulation of organic acid synthesis in K. pneumoniae. Besides, rpoS deletion eliminated the formation of 2,3-butanediol, the main byproduct produced during 1,3-propanediol fermentation, indicating the role of rpoS in metabolic regulation in K. pneumoniae. Thus, a K. pneumoniae mutant was developed, which could produce lower organic acid during 1,3-propanediol fermentation due to an rpoS mutation in this study.

Schistosomiasis in the People's Republic of China - down but not out.

Schistosomiasis has been subjected to extensive control efforts in the People's Republic of China (China) which aims to eliminate the disease by 2030. We describe baseline results of a longitudinal cohort study undertaken in the Dongting and Poyang lakes areas of central China designed to determine the prevalence of Schistosoma japonicum in humans, animals (goats and bovines) and Oncomelania snails utilizing molecular diagnostics procedures. Data from the Chinese National Schistosomiasis Control Programme (CNSCP) were compared with the molecular results obtained.Sixteen villages from Hunan and Jiangxi provinces were surveyed; animals were only found in Hunan. The prevalence of schistosomiasis in humans was 1.8% in Jiangxi and 8.0% in Hunan determined by real-time polymerase chain reaction (PCR), while 18.3% of animals were positive by digital droplet PCR. The CNSCP data indicated that all villages harboured S. japonicum-infected individuals, detected serologically by indirect haemagglutination assay (IHA), but very few, if any, of these were subsequently positive by Kato-Katz (KK).Based on the outcome of the IHA and KK results, the CNSCP incorporates targeted human praziquantel chemotherapy but this approach can miss some infections as evidenced by the results reported here. Sensitive molecular diagnostics can play a key role in the elimination of schistosomiasis in China and inform control measures allowing for a more systematic approach to treatment.

A new and effective genes-based method for phylogenetic analysis of Klebsiella pneumoniae.

The exponential increase in the number of genomes deposited in public databases can help us gain a more holistic understanding of the phylogeny and epidemiology of Klebsiella pneumoniae. However, inferring the evolutionary relationships of K. pneumoniae based on big genomic data is challenging for existing methods. In this study, core genes of K. pneumoniae were determined and analysed in terms of differences in GC content, mutation rate, size, and potential functions. We then developed a stable genes-based method for big data analysis and compared it with existing methods. Our new method achieved a higher resolution phylogenetic analysis of K. pneumoniae. Using this genes-based method, we explored global phylogenetic relationships based on a public database of nearly 953 genomes. The results provide useful information to facilitate the phylogenetic and epidemiological analysis of K. pneumoniae, and the findings are relevant for security applications.

An Ensemble Learning Method for Robot Electronic Nose with Active Perception.

The electronic nose is the olfactory organ of the robot, which is composed of a large number of sensors to perceive the smell of objects through free diffusion. Traditionally, it is difficult to realize the active perception function, and it is difficult to meet the requirements of small size, low cost, and quick response that robots require. In order to address these issues, a novel electronic nose with active perception was designed and an ensemble learning method was proposed to distinguish the smell of different objects. An array of three MQ303 semiconductor gas sensors and an electrochemical sensor DART-2-Fe5 were used to construct the novel electronic nose, and the proposed ensemble learning method with four algorithms realized the active odor perception function. The experiment results verified that the accuracy of the active odor perception can reach more than 90%, even though it used 30% training data. The novel electronic nose with active perception based on the ensemble learning method can improve the efficiency and accuracy of odor data collection and olfactory perception.

Machine learning algorithms to predict the 1 year unfavourable prognosis for advanced schistosomiasis.

Short-term prognosis of advanced schistosomiasis has not been well studied. We aimed to construct prognostic models using machine learning algorithms and to identify the most important predictors by utilising routinely available data under the government medical assistance programme. An established database of advanced schistosomiasis in Hunan, China was utilised for analysis. A total of 9541 patients for the period from January 2008 to December 2018 were enrolled in this study. Candidate predictors were selected from demographics, clinical features, medical examinations and test results. We applied five machine learning algorithms to construct 1 year prognostic models: logistic regression (LR), decision tree (DT), random forest (RF), artificial neural network (ANN) and extreme gradient boosting (XGBoost). An area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. The important predictors of the optimal model for unfavourable prognosis within 1 year were identified and ranked. There were 1249 (13.1%) cases having unfavourable prognoses within 1 year of discharge. The mean age of all participants was 61.94 years, of whom 70.9% were male. In general, XGBoost showed the best predictive performance with the highest AUC (0.846; 95% confidence interval (CI): 0.821, 0.871), compared with LR (0.798; 95% CI: 0.770, 0.827), DT (0.766; 95% CI: 0.733, 0.800), RF (0.823; 95% CI: 0.796, 0.851), and ANN (0.806; 95% CI: 0.778, 0.835). Five most important predictors identified by XGBoost were ascitic fluid volume, haemoglobin (HB), total bilirubin (TB), albumin (ALB), and platelets (PT). We proposed XGBoost as the best algorithm for the evaluation of a 1 year prognosis of advanced schistosomiasis. It is considered to be a simple and useful tool for the short-term prediction of an unfavourable prognosis for advanced schistosomiasis in clinical settings.

Prevalence and incidence of advanced schistosomiasis and risk factors for case fatality in Hunan Province, China.

Advanced schistosomiasis has become a major public health problem in areas with a heavy burden of schistosomiasis infection. Our objective was to determine the incidence and prevalence of advanced schistosomiasis and risk factors associated with case fatality of advanced schistosomiasis. Data were abstracted from hospitalization records of patients with advanced schistosomiasis from Hunan Province, China. The incidence and prevalence of advanced schistosomiasis were determined and the risk factors for death in advanced patients were assessed using logistic regression analysis. A total of 10,362 patients with advanced schistosomiasis were recruited into our study and 65% of them were categorized as the ascites type. There were 1249 deaths between 2005 and 2018 and the case fatality was 12.05%. The incidence of advanced schistosomiasis increased from 2002 to 2010, peaked in 2010 and then leveled off. The prevalence of advanced schistosomiasis increased from 2005 to 2014, and was stable afterwards. HBV was a risk factor for death in advanced patients (adjusted odds ratio (aOR=1.93, 95% confidence interval (CI: 1.55 to 2.41). Patients without splenectomy had a higher risk of death (aOR=1.29, 95%CI: 1.08 to 1.56). Upper gastrointestinal bleeding was positively associated with the risk of death (aOR=1.42, 95% CI: 1.15 to 1.76). Besides, abnormal ALT, ascites and anemia were also significantly associated with the risk of death in advanced patients. Advanced schistosomiasis was effectively controlled in recent years. Splenectomy could reduce the case fatality of advanced patients. HBV infection, abnormal ALT, upper gastrointestinal bleeding and anemia also predicted the risk of death for advanced patients.

Spatio-temporal variations of emerging sites infested with schistosome-transmitting Oncomelania hupensis in Hunan Province, China, 1949-2016.

BACKGROUND: Constant emerging sites infested with Oncomelania hupensis (O. hupensis) impede the goal realization of eliminating schistosomiasis. The study assessed the spatial and temporal distributions of new Oncomelania snail habitats in Hunan Province from 1949 to 2016. METHODS: We used the data from annual snail surveys throughout Hunan Province for the period from 1949 to 2016. Global Moran's I, Anselin local Moran's I statistics (LISA) and a retrospective space-time permutation model were applied to determine the spatial and temporal distributions of emerging snail-infested sites. RESULTS: There were newly discovered snail-infested sites almost every year in 1949-2016, except for the years of 1993, 2009 and 2012. The number of emerging sites varied significantly in the five time periods (1949-1954, 1955-1976, 1977-1986, 1986-2003 and 2004-2016) (H = 25.35, p < 0.05). The emerging sites lasted 37.52 years in marshlands, 30.04 years in hills and 24.63 at inner embankments on average, with the values of Global Moran's I being 0.52, 0.49 and 0.44, respectively. High-value spatial clusters (HH) were mainly concentrated along the Lishui River and in Xiangyin County. There were four marshland clusters, two hill clusters and three inner embankment clusters after 1976. CONCLUSIONS: Lower reaches of the Lishui River and the Dongting Lake estuary were the high-risk regions for new Oncomelania snail habitats with long durations. Snail surveillance should be strengthened at stubborn snail-infested sites at the inner embankments. Grazing prohibition in snail-infested grasslands should be a focus in marshlands. The management of bovines in Xiangyin County is of great importance.

A modelling algorithm for amorphous covalent triazine-based polymers.

Rational and purposeful designs of amorphous materials with desirable structures are difficult to implement due to the complex and unordered nature of such materials. In this work, a modelling algorithm was proposed for amorphous covalent triazine-based polymers to construct atomistic representative models that can reproduce the experimentally measured properties of experimental samples. The constructed models were examined through comparisons of simulated and experimental properties, such as surface area, pore volume, and structure factor, and further validated by the good consistency observed among these properties. To assess the predictive capability of the modelling algorithm, we used a new covalent triazine-based polymer and predicted its porosity by constructing a simulated model. The predicted results on the surface area and pore volume of the simulated model were quantitatively consistent with the experimental data derived from the experimentally synthesized sample. This consistency reveals the predictive capacity of the proposed modelling algorithm. The algorithm could be a promising approach to predict and develop advanced covalent triazine-based polymers for multiple applications.

Atomistic structure generation of covalent triazine-based polymers by molecular simulation.

The structures of amorphous materials are generally difficult to characterize and comprehend due to their unordered nature and indirect measurement techniques. However, molecular simulation has been considered as an alternative method that can provide molecular-level information supplementary to experimental techniques. In this work, a new approach for modelling the atomistic structures of amorphous covalent triazine-based polymers is proposed and employed on two experimentally synthesized covalent triazine-based polymers. To examine the proposed modelling approach, the properties of the established models, such as surface areas, pore volumes, structure factors and N2 adsorption isotherms, were calculated and compared with the experimental data. Excellent consistencies were observed between the simulated models and experimental samples, consequently validating the proposed models and the modelling approach. Moreover, the proposed modelling approach can be applied to new covalent triazine-based polymers for predictive purposes and to provide design strategies for future synthesis works.