Incidence trends of gastric cancer in the United States over 2000-2020: A population-based analysis.

BACKGROUND: Gastric cancer ranks among the top cancers in terms of both occurrence and death rates in the United States (US). Our objective was to provide the incidence trends of gastric cancer in the US from 2000 to 2020 by age, sex, histology, and race/ethnicity, and to evaluate the effects of the COVID-19 pandemic. METHODS: We obtained data from the Surveillance, Epidemiology, and End Results 22 program. The morphologies of gastric cancer were classified as adenocarcinoma, gastrointestinal stromal tumor, signet ring cell carcinoma, and carcinoid tumor. We used average annual percent change (AAPC) and compared pairs using parallelism and coincidence. The numbers were displayed as both counts and age-standardized incidence rates (ASIRs) per 100000 individuals, along with their corresponding 95% confidence intervals (CIs). RESULTS: Over 2000-2019, most gastric cancers were among those aged ≥55 years (81.82%), men (60.37%), and Non-Hispanic Whites (62.60%). By histology, adenocarcinoma had the highest incident cases. During the COVID-19 pandemic, there was a remarkable decline in ASIRs of gastric cancer in both sexes and all races (AAPC: -8.92; 95% CI: -11.18 to -6.67). The overall incidence trends of gastric cancer were not parallel, nor identical. CONCLUSIONS: The incidence of gastric cancer shows notable variations by age, race, and sex, with a rising trend across ethnicities. While the overall incidence has declined, a noteworthy increase has been observed among younger adults, particularly young Hispanic women; however, rates decreased significantly in 2020.

Network analysis to identify driver genes and combination drugs in brain cancer.

Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.

Use of fusion imaging in safe, rapid, and accurate placement of percutaneous right ventricular assist device in the management of acute post-surgical right ventricular failure.

Fusion imaging (FI) technology using EchoNavigator that integrates live transesophageal echocardiogram and overlays on real-time fluoroscopy. We present our experience placing a right ventricular (RV) support device, a ProtekDuo, in our patient with post-operative RV failure using FI to guide the implantation.

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Urinary MicroRNA biomarkers of nephrotoxicity in Macaca fascicularis.

Drug-induced kidney injury (DIKI) refers to kidney damage resulting from the administration of medications. The aim of this project was to identify reliable urinary microRNA (miRNAs) biomarkers that can be used as potential predictors of DIKI before disease diagnosis. This study quantified a panel of six miRNAs (miRs-210-3p, 423-5p, 143-3p, 130b-3p, 486-5p, 193a-3p) across multiple time points using urinary samples from a previous investigation evaluating effects of a nephrotoxicant in cynomolgus monkeys. Exosome-associated miRNA exhibited distinctive trends when compared to miRNAs quantified in whole urine, which may reflect a different urinary excretion mechanism of miRNAs than those released passively into the urine. Although further research and mechanistic studies are required to elucidate how these miRNAs regulate signaling in disease pathways, we present, for the first time, data that several miRNAs displayed strong correlations with histopathology scores, thus indicating their potential use as biomarkers to predict the development of DIKI in preclinical studies and clinical trials. Also, these findings can potentially be translated into other non-clinical species or human for the detection of DIKI.

Flavonoid compounds and their synergistic effects: Promising approaches for the prevention and treatment of psoriasis with emphasis on keratinocytes - A systematic and mechanistic review.

Psoriasis, a chronic autoimmune skin disorder, causes rapid and excessive skin cell growth due to immune system dysfunction. Numerous studies have shown that flavonoids have anti-psoriatic effects by modulating various molecular mechanisms involved in inflammation, cytokine production, keratinocyte proliferation, and more. This study reviewed experimental data reported in scientific literature and used network analysis to identify the potential biological roles of flavonoids' targets in treating psoriasis. 947 records from Web of Sciences, ScienceDirect database, Scopus, PubMed, and Cochrane library were reviewed without limitations until June 26, 2023. 66 articles were included in the systematic review. The ten genes with the highest scores, including interleukin (IL)-10, IL-12A, IL-1ß, IL-6, Tumor necrosis factor-α (TNF-α), Janus kinase 2 (JAK 2), Jun N-terminal kinase (JUN), Proto-oncogene tyrosine-protein kinase Src (SRC), Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and Signal transducer and activator of transcription 3 (STAT3), were identified as the hub genes. KEGG pathway analysis identified connections related to inflammation and autoimmune responses, which are key characteristics of psoriasis. IL-6, STAT3, and JUN's presence in both hub and enrichment genes suggests their important role in flavonoid's effect on psoriasis. This comprehensive study highlights how flavonoids can target biological processes in psoriasis, especially when combined for enhanced effectiveness.

Bioconversion of cow manure through vermicomposting: effects of tylosin concentration on the weight of worms and manure quality.

This study investigated batch-fed vermicomposting of cow manure, with a specific focus on assessing the effects of tylosin on the weight of earthworms and the overall quality of the resulting manure. Five reactors, including three concentrations of tylosin (50, 100, and 150 mg/kg) and two control reactors, were employed. Residual tylosin concentrations were measured using high-performance liquid chromatography (HPLC). Quality parameters such as pH, temperature, volatile solids (VS), organic carbon content (OCC), electrical conductivity (EC), ash content, C/N ratio, total Kjeldahl nitrogen (TKN), and microbial content were evaluated. The toxicity and maturity of vermicompost were assessed by determining the germination index (GI). The study also monitored variations in the earthworm's weight. The results demonstrated a decreasing trend in VS, OCC, C/N, and fecal coliforms, along with increased pH, EC, ash content, and TKN during the vermicomposting process. Furthermore, investigations revealed significant reductions in the reactors with tylosin concentrations of 50, 100, and 150 mg/kg, resulting in the removal of 98%, 90.48%, and 89.38% of the initial tylosin, respectively. This result confirms the faster removal of tylosin in reactors with lower concentrations. Degradation of tylosin also conforms to first-order kinetics. The findings showed a significant influence of tylosin on the weight of Eisenia fetida earthworms and the lowest antibiotic concentration led to the highest weight gain. Finally, the high percentage of germination index (90-100%) showed that the quality and maturity of vermicompost is by national and international standards.

The efficacy of curcumin supplementation on serum total antioxidant capacity, malondialdehyde, and disease activity in women with rheumatoid arthritis: A randomized, double-blind, placebo-controlled clinical trial.

Oxidative stress plays a crucial role in the physiopathology of rheumatoid arthritis (RA), which is associated with impaired antioxidant defenses. This study aimed to investigate the effects of curcumin supplementation on serum levels of total antioxidant capacity (TAC), malondialdehyde (MDA), and disease activity in women with RA. In this clinical trial, 48 women with RA were treated with one capsule of curcumin (500 mg daily) or placebo for 8 weeks. Anthropometric measurements and fasting blood samples were collected at baseline and end of the study. Finally, we assessed the Disease Activity Score in 28 joints (DAS-28), dietary intake, and physical activity levels. While curcumin supplementation for 8 weeks significantly increased the serum levels of TAC (p < 0.05), it decreased tender joint counts, swollen joint counts, visual analog scale (VAS) for pain, and DAS-28 compared to the placebo at the end of the study (p < 0.001 for all). MDA levels significantly decreased in the curcumin group (p < 0.05). However, changes in MDA concentration were not significant between groups at the end of the trial (p = 0.145). Curcumin supplementation had a beneficial effect on increasing the serum levels of TAC and decreased DAS-28 in women with RA.

Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

BACKGROUND: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients' smoking status from their EHRs. METHODS: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients' smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into 'Smoker' and 'Non-Smoker' with further classifications as 'Active-Smoker', 'Former-Smoker', and 'Never-Smoker'. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. RESULTS: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91-92% for Never-Smoker. CONCLUSION: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model's capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Investigating the improvement of the quality of industrial effluents for reuse with added processes: coagulation, flocculation, multi-layer filter and UV.

Reuse of wastewater is one of the ways to develop water resources. In addition to the need for drinking water, many industries also need high-quality water in the production line. Therefore, the purpose of the present study is to investigate the advanced treatment of the wastewater treatment plant of Morche Khort industrial town using the processes of coagulation, flocculation with aeration, multi-layer filter, and disinfection by ultraviolet radiation to increase the quality of wastewater and reuse it in industries. In this study, to investigate the effect of coagulation and flocculation units along with aeration, filtration, and disinfection by ultraviolet rays (UV), on the quality of the secondary effluent from the wastewater treatment plant of Morche Khort industrial town, they were operated on a pilot scale. Polyaluminum chloride (PAC) was used as a coagulant. Layering of three layers of sand filter, from bottom to top including granulated silica at a height of 10 cm, sand at a height of 20 cm, and activated carbon at a height of 70 cm was used. The input and output sampling points of each unit were considered. By repeating twice in five stages of flow rates of 1, 2, 4, 6, and 8 (L/min), the samples were collected to determine COD, TSS, TDS, turbidity, pH, hardness, total coliform, and fecal coliform. Jar test results showed that Alum coagulant works almost the same as PAC in removing turbidity, but the efficiency of removing organic substances by PAC coagulant is higher than that of Alum at lower doses. The results of this study showed that the efficiency of the coagulation and flocculation process in removing turbidity, COD, TSS, TDS, and fat was 56.88%, 46.66%, 38%, 23.19%, and 91.43% respectively. In the current study, the results of the wastewater entering the sand filter showed that the percentage of removal efficiency with a loading rate of 1 (L/min) was turbidity, TSS, COD, TDS, and fat was 16. 93%, 56.84%, 50%, 5.67%, 33.44% respectively. In the UV disinfection unit, the removal efficiency percentage with a loading rate of 1 (L/min) for COD, TSS, turbidity, hardness, total coliform, and fecal coliform is 16%, 3.45%, 3.58%, 5.21%, 99.88%, and 98.37% respectively. Coagulation and flocculation system-multi-layer filter and disinfection can remove chemical-physical and microbial parameters to an acceptable level for using water in advanced purification systems and also for irrigation.

Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.

Corona virus 19 (Covid-19) has caused many problems in public health, economic, and even cultural and social fields since the beginning of the epidemic. However, in order to provide therapeutic solutions, many researches have been conducted and various omics data have been published. But there is still no early diagnosis method and comprehensive treatment solution. In this manuscript, by collecting important genes related to COVID-19 and using centrality and controllability analysis in PPI networks and signaling pathways related to the disease; hub and driver genes have been identified in the formation and progression of the disease. Next, by analyzing the expression data, the obtained genes have been evaluated. The results show that in addition to the significant difference in the expression of most of these genes, their expression correlation pattern is also different in the two groups of COVID-19 and control. Finally, based on the drug-gene interaction, drugs affecting the identified genes are presented in the form of a bipartite graph, which can be used as the potential drug combinations.

Synthesis, design, biological evaluation, and computational analysis of some novel uracil-azole derivatives as cytotoxic agents.

The design and synthesis of novel cytotoxic agents is still an interesting topic for medicinal chemistry researchers due to the unwanted side effects of anticancer drugs. In this study, a novel series of uracil-azole hybrids were designed and synthesized. The cytotoxic activity, along with computational studies: molecular docking, molecular dynamic simulation, density functional theory, and ADME properties were also, evaluated. The compounds were synthesized by using 3-methyl-6-chlorouracil as the starting material. Cytotoxicity was determined using MTT assay in the breast carcinoma cell line (MCF-7) and Hepatocellular carcinoma cell line (HEPG-2). These derivatives demonstrated powerful inhibitory activity against breast and hepatocellular carcinoma cell lines in comparison to Cisplatin as positive control. Among these compounds, 4j displayed the best selectivity profile and good activity with IC50 values of 16.18 ± 1.02 and 7.56 ± 5.28 µM against MCF-7 and HEPG-2 cell lines respectively. Structure-activity relationships revealed that the variation in the cytotoxic potency of the synthesized compounds was affected by various substitutions of benzyl moiety. The docking output showed that 4j bind well in the active site of EGFR and formed a stable complex with the EGFR protein. DFT was used to investigate the reactivity descriptors of 4a and 4j. The outputs demonstrated that these uracil-azole hybrids can be considered as potential cytotoxic agents.

Comparative immunogenicity and safety of SpikoGen®, a recombinant SARS-CoV-2 spike protein vaccine in children and young adults: An immuno-bridging clinical trial.

BACKGROUND: SpikoGen® is a recombinant subunit spike protein ectodomain vaccine manufactured in insect cells and formulated with the novel polysaccharide-based Advax-CpG55.2 adjuvant. This study aimed to compare the immunogenicity and safety of SpikoGen® vaccine in children, adolescents and young adults. METHODS: This was a non-randomized, three-arm, open-label, parallel-group, immuno-bridging, non-inferiority trial to compare the immunogenicity and safety of a primary course of two intramuscular doses of SpikoGen® vaccine in children aged 5 to < 12 years, adolescents aged 12 to < 18 years and young adults aged 18 to 40 years. Children 5-12 years received a half dose of 12.5 µg spike protein, whereas the other groups received the full vaccine dose. Vaccine immunogenicity was evaluated via assessment of serum anti-spike and neutralizing antibodies 14 days after the second dose. Solicited adverse events were recorded for 7 days after each vaccination. Safety assessments including serious adverse events were continued through six months after the second dose in children and adolescents. RESULTS: Two weeks after the second dose, seroconversion rates for neutralizing antibody levels were not significantly different for children (59.50 %), adolescents (52.06 %) and adults (56.01 %). The 95 % confidence interval of the difference in seroconversion rates between children and adults was within the prespecified non-inferiority margin of 10 % (-12 % to 5 %). SpikoGen® vaccine was well tolerated in all age groups with the most common solicited adverse events being injection site pain and fatigue which were generally transient and mild. CONCLUSION: SpikoGen® vaccine was shown to be safe, well tolerated and immunogenic in children as young as 5 years of age, with non-inferior responses to those seen in adults. The Iranian FDA authorisation of SpikoGen® vaccine is now extended down to 5 years of age.

The utility of ultraviolet beam in advanced oxidation-reduction processes: a review on the mechanism of processes and possible production free radicals.

Advanced oxidation processes (AOPs) and advanced reduction processes (ARPs) are a set of chemical treatment procedures designed to eliminate organic (sometimes inorganic) contamination in water and wastewater by producing free reactive radicals (FRR). UV irradiation is one of the factors that are effectively used in oxidation-reduction processes. Not only does the UV beam cause the photolysis of contamination, but it also leads to the product of FRR by affecting oxidants-reductant, and the pollutant decomposition occurs by FRR. UV rays produce active radical species indirectly in an advanced redox process by affecting an oxidant (O3, H2O2), persulfate (PS), or reducer (dithionite, sulfite, sulfide, iodide, ferrous). Produced FRR with high redox potential (including oxidized or reduced radicals) causes detoxification and degradation of target contaminants by attacking them. In this review, it was found that ultraviolet radiation is one of the important and practical parameters in redox processes, which can be used to control a wide range of impurities.

AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.

BACKGROUND: Alcohol use disorder (AUD) causes significant morbidity, mortality, and injuries. According to reports, approximately 5% of all registered deaths in Denmark could be due to AUD. The problem is compounded by the late identification of patients with AUD, a situation that can cause enormous problems, from psychological to physical to economic problems. Many individuals suffering from AUD never undergo specialist treatment during their addiction due to obstacles such as taboo and the poor performance of current screening tools. Therefore, there is a lack of rapid intervention. This can be mitigated by the early detection of patients with AUD. A clinical decision support system (DSS) powered by machine learning (ML) methods can be used to diagnose patients' AUD status earlier. METHODS: This study proposes an effective AUD prediction model (AUDPM), which can be used in a DSS. The proposed model consists of four distinct components: (1) imputation to address missing values using the k-nearest neighbours approach, (2) recursive feature elimination with cross validation to select the most relevant subset of features, (3) a hybrid synthetic minority oversampling technique-edited nearest neighbour approach to remove noise and balance the distribution of the training data, and (4) an ML model for the early detection of patients with AUD. Two data sources, including a questionnaire and electronic health records of 2571 patients, were collected from Odense University Hospital in the Region of Southern Denmark for the AUD-Dataset. Then, the AUD-Dataset was used to build ML models. The results of different ML models, such as support vector machine, K-nearest neighbour, decision tree, random forest, and extreme gradient boosting, were compared. Finally, a combination of all these models in an ensemble learning approach was selected for the AUDPM. RESULTS: The results revealed that the proposed ensemble AUDPM outperformed other single models and our previous study results, achieving 0.96, 0.94, 0.95, and 0.97 precision, recall, F1-score, and accuracy, respectively. In addition, we designed and developed an AUD-DSS prototype. CONCLUSION: It was shown that our proposed AUDPM achieved high classification performance. In addition, we identified clinical factors related to the early detection of patients with AUD. The designed AUD-DSS is intended to be integrated into the existing Danish health care system to provide novel information to clinical staff if a patient shows signs of harmful alcohol use; in other words, it gives staff a good reason for having a conversation with patients for whom a conversation is relevant.

Catalytic electrodes' characterization study serving polluted water treatment: environmental healthcare and ecological risk assessment.

Pesticide residues in the environment have irreparable effects on human health and other organisms. Hence, it is necessary to treat and degrade them from polluted water. In the current work, the electrochemical removal of the fenitrothion (FT), trifluralin (TF), and chlorothalonil (CT) pesticides were performed by catalytic electrode. The characteristics of SnO2-Sb2O3, PbO2, and Bi-PbO2 electrodes were described by FE-SEM and XRD. Dynamic electrochemical techniques including cyclic voltammetry, electrochemical impedance spectroscopy, accelerated life, and linear polarization were employed to investigate the electrochemical performance of fabricated electrodes. Moreover, evaluate the risk of toxic metals release from the catalytic electrode during treatment process was investigated. The maximum degradation efficiency of 99.8, 100, and 100% for FT, TF, and CT was found under the optimal condition of FT, TF, and CT concentration 15.0 mg L-1, pH 7.0, current density 7.0 mA cm-2, and electrolysis time of 120 min. The Bi-PbO2, PbO2, and SnO2-Sb2O3 electrodes revealed the oxygen evolution potential of 2.089, 1.983, 1.914 V, and the service lifetime of 82, 144, and 323 h, respectively. The results showed that after 5.0 h of electrolysis, none of the heavy metals such as Bi, Pb, Sb, Sn, and Ti were detected in the treated solution.

Treatment with quercetin increases Nrf2 expression and neuronal differentiation of sub ventricular zone derived neural progenitor stem cells in adult rats.

BACKGROUND: The presence of neural precursor stem cells (NPSCs) in some parts of the adult brain and the potency of these types of cells with a therapeutic viewpoint, has opened up a new approach for the treatment and recovery of the defects of central nervous system (CNS). Quercetin, as an herbal flavonoid, has been extensively investigated and shown to have numerous restoratives, inhibitory, and protective effects on some cell-lines and disorders. The purpose of this study is to simultaneously investigate the effect of quercetin on the expression of the nuclear factor erythroid 2-related factor 2 (Nrf2) gene and the effect on the proliferation and differentiation of NPSCs derived from the subventricular zone (SVZ) of the brain of adult rats. METHODS AND RESULTS: The cell obtained from SVZ cultured for one week and treated with quercetin at the concentrations of 1, 5, and 15 µM to evaluate the Nrf2 expression, proliferation and differentiation of NSCs after one week. Cellular and genetic results was performed by RT-PCR, MTT assay test, quantification of images with Image-J and counting. The results indicated that the quercetin increases expression of Nrf2 at concentration above 5 µM. Also differentiation and proliferation rate of NSCs is affected by various concentrations of quercetin in a dose-dependent manner. CONCLUSION: These findings confirmed the dose-dependent effect of quercetin on proliferation and differentiation of cell. In addition, quercetin increased the expression of Nrf2 gene. By combining these two effects of quercetin, this substance can be considered an effective compound in the treatment of degenerative defects in CNS.

Application of artificial neural network and dynamic adsorption models to predict humic substances extraction from municipal solid waste leachate.

Sustainable municipal solid waste leachate (MSWL) management requires a paradigm shift from removing contaminants to effectively recovering resources and decreasing contaminants simultaneously. In this study, two types of humic substances, fulvic acid (FA) and humic acid (HA) were extracted from MSWL. HA was extracted using HCl and NaOH solution, followed by FA using a column bed under diversified operations such as flow rate, input concentration, and bed height. Also, this work aims to evaluate efficiency of Artificial Neural Network (ANN) and Dynamic adsorption models in predicting FA. With the flow rate of 0.3 mL/min, bed height of 15.5 cm, and input concentration of 4.27 g/mL, the maximum capacity of FA was obtained at 23.03 mg/g. FTIR analysis in HA and FA revealed several oxygen-containing functional groups including carboxylic, phenolic, aliphatic, and ketone. The high correlation coefficient value (R2) and a lower mean squared error value (MSE) were obtained using the ANN, indicating the superior ability of ANN to predict adsorption capacity compared to traditional modeling.