Unveiling the Neuroprotective Mechanisms of Centella Asiatica Extract Delivered via pEG Nanoparticles in TNF-α-Induced Cognitive Decline: An In Vivo Study

Unveiling the Neuroprotective Mechanisms of Centella Asiatica Extract Delivered via pEG Nanoparticles in TNF-α-Induced Cognitive Decline: An In Vivo Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unveiling the Neuroprotective Mechanisms of Centella Asiatica Extract Delivered via pEG Nanoparticles in TNF-α-Induced Cognitive Decline: An In Vivo Study Nathania Nathania, Marson Putra, Agoes Willyono, Fransiskus Xaverius Rinaldi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5987342/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Cognitive decline in the elderly is a pressing global health concern, affecting 65.6 million individuals worldwide and significantly diminishing quality of life. Elevated inflammatory markers, particularly Tumor Necrosis Factor-alpha (TNF-α), are strongly associated with neuronal damage and cognitive deterioration. Emerging evidence highlights the neuroprotective potential of natural compounds, such as those in Centella asiatica extract, known for its pharmaceutical benefits in addressing neurodegenerative diseases. Aim The research aims to evaluate the formulation’s impact on TNF-α expression and cognitive performance, focusing on its neuroprotective properties. By exploring this novel approach, the study seeks to contribute valuable insights into managing age-related cognitive decline, emphasizing TNF-α regulation as a potential therapeutic target. Results and Discussion The GC-MS analysis identified active compounds in Centella asiatica extract, including tryptamine (1.79%), γ-sitosterol, and β-sitosterol (each 7.22%), essential for cognitive improvement. These findings confirm the presence of neuroprotective agents prior to synthesis with PEG-400. On PSA, the 1:100 extract-to-PEG-400 ratio produced ideal nanoparticles (20–25 nm) optimal for blood-brain barrier (BBB) penetration. Smaller nanoparticles in this range demonstrate effective therapeutic delivery, minimizing immune clearance and degradation. T-maze tests revealed significant cognitive improvements in the 1:1 and 1:100 groups, with the latter showing the most consistent results. Flow cytometry indicated a dose-dependent reduction in TNF-α expression, with the 1:100 group achieving the greatest decrease (54.59%) compared to the control (74.65%). Conclusion The 1:100 (Centella:pEG) formulation demonstrated superior stability, bioavailability, and efficacy due to optimized nanoparticle size and consistent compound delivery, supporting Centella asiatica encapsulation as a promising neurotherapeutic strategy. Biological sciences/Neuroscience Biological sciences/Plant sciences Health sciences/Molecular medicine Health sciences/Neurology Physical sciences/Nanoscience and technology Cognitive impairment Nanoparticle in vivo centella asiatica herbal neurodegenerative Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Cognitive decline in the elderly is a significant global health concern, affecting 65.6 million individuals worldwide and reducing quality of life [ 1 ],[ 2 ]. Chronic inflammation in the brain, driven by pro-inflammatory cytokines like TNF-α (Tumor Necrosis Factor-alpha), plays a central role in cognitive decline and neurodegenerative diseases such as Alzheimer's disease. Elevated TNF-α levels activate microglia, the brain's immune cells, which release further inflammatory signals that can disrupt synaptic function, impair neuroplasticity, and promote neuronal damage [ 3 ]. This inflammatory cascade contributes to the formation of amyloid beta plaques and tau tangles, hallmark features of Alzheimer's pathology. Additionally, TNF-α can compromise the blood-brain barrier, allowing harmful immune cells and molecules to infiltrate the brain. As a result, sustained neuroinflammation accelerates cognitive decline. Therapeutic strategies targeting TNF-α or modulating neuroinflammation are being explored as potential treatments to slow or halt the progression of neurodegenerative diseases, although challenges remain in delivering these therapies effectively to the brain [ 4 ]. In recent years, researchers have focused on the neuroprotective properties of natural compounds found in Centella asiatica extract, highlighting its potential in addressing neurodegenerative diseases. Secondary metabolites, such as tryptamine, an active metabolite of tryptophan have been linked to processes like cholinesterase inhibition and neuroprotection [ 5 ]. Centella asiatica has been recognized as a promising herbal remedy for cognitive impairment, supported by its pharmaceutical potential [ 6 ]. This validation is further strengthened by [ 7 ], who demonstrated that Centella asiatica improves the structure of the brain by enhancing dendritic branching in hippocampal neurons, a region critical for cognitive function. Additionally, asiaticoside, a key compound in Centella asiatica, has shown the ability to promote brain regeneration by reducing neuroinflammation [ 8 ]. It achieves this through the suppression of proinflammatory cytokines, such as TNF-α, and modulation of brain-derived neurotrophic factor (BDNF), making it a compelling candidate for addressing age-related cognitive decline. By coating nanoparticles with PEG, a hydrophilic layer is created that keeps the particles from aggregating and stabilizes them in a variety of biological settings [ 9 ]. The drug's half-life in the body is prolonged by this stabilization, which also shields the nanoparticles from immune system identification and premature breakdown [ 10 ]. Furthermore, PEG increases the solubility of hydrophobic medications, boosting their bioavailability and facilitating easier absorption [ 11 ]. Additionally, PEGylation gives nanoparticles "stealth" qualities that help them avoid detection by the immune system and stay in the bloodstream longer, increasing their capacity to target specific areas. This study demonstrates the efficacy of Centella asiatica encapsulated in PEG 400 nanoparticles in reducing TNF-alpha expression. The nanoparticle-based drug delivery system of pEG400 system improve drugs efficacy in central nervous system (CNS) by decreasing the density and viscosity of the solution, and improve the dispersibility of the particle [ 12 ]. Findings from recent study showed that PEG400-coated curcumin substantially decreases its toxicity and enhances its bioavailability. This improvement leads to increased antioxidant and anti-inflammatory activity, thereby providing greater therapeutic potential against neurological disorders [ 13 ]. Aim This study examines the cognitive advantages of a synthesized extract of Centella asiatica that is coated with nanoparticles of PEG 400. This research attempts to clarify the potential neuroprotective effects of this new formulation by studying the connection between TNF-α levels and cognitive performance. 2. METHODS 2.1 Ethical Clearance We obtained ethical approval for our study from the Faculty of Medicine, Universitas Airlangga (Approval No. 38/EC/KEPK/FKUA/2024), in accordance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines. All experimental procedures were conducted following relevant animal welfare regulations and ethical considerations for research involving live vertebrates. 2.2 Synthesis of Centella and pEG 400 The synthesis process of the CA extract formulation with PEG-400 involves dividing the formulation into three test groups (1:1, 1:100, 100:1). This comparison aims to optimize the variation in concentrations based on the proportion between the CA extract and PEG-400. The three test groups are as follows: group 1 (1 CA : 1 PEG-400); group 2 (1 CA : 100 PEG-400); and group 3 (100 CA : 1 PEG-400). By systematically analyzing these three ratios, the research aims to identify the most effective and stable formulation for therapeutic applications. These formulations are stirred using a magnetic stirrer for 15 minutes at a speed of 1,500 rpm and sonicated with a Scientz ultrasonicator for 5 minutes with 10-second pulse on and 5-second pulse off intervals. 2.3 Particle Size Analyzer Once we have collected samples from 3 different groups, we then proceed to evaluate their properties. Particle characterisation technologies have made significant advancements, providing a more profound understanding of powder systems and blends. Instruments capable of revealing the size and shape of particulate systems can offer much more detailed information about the particles being examined compared to traditional particle size measurements which utilize particle size analyzer [ 14 ]. The PSA with BK-802N DLS nanoparticle size analyser biobase were conducted in Laboratory of Lembaga Ilmu Hayati, Teknik dan Rekayasa (LIHTR) Universitas Airlangga for each concentration (1:1; 1:100; 100:1). 2.4 Animal Preparation BALB/c male mice (Mus musculus, approximately 35.08 g) were obtained from BBVF Pusvetma, Surabaya, Indonesia, and utilized for in vivo experiments. All experimental procedures were performed in accordance with approved protocols by the Faculty of Medicine, Universitas Airlangga Animal Care and Use Committee (IACUC). The study was carried out in compliance with the ARRIVE guidelines. The treatment group was divided into four subgroups, each containing five mice. These subgroups included a control group and three dose groups (Centella:pEG 400) with ratios of 1:1, 1:100, and 100:1 (Fig. 1 ). After a 7-day acclimatization period, all mice groups (except control) received lipopolysaccharide injection, in order to trigger neuroinflammation, leading to neuronal damage. After 7 days, each treatment group received a 500µL oral injection of the extract for the three dose groups over a period of 14 days. 2.5 T Maze Test The T Maze Test is chosen as the assessment tool for cognitive performance in mice due to its lack of requirement for pre-training and its reliance exclusively on the inherent attraction of rodents to novelty and their spontaneous actions [ 15 ], [ 16 ]. The T Maze test (Fig. 2 ) for mice measures spatial and memory functions, helping to detect hippocampal dysfunction [ 17 ]. Test model is performed in a plastic enclosure with 600 mm x 165 mm starting arm and 400 mm x 100 mm goal arms at the upper apex of the "T" [ 18 ]. The configuration is positioned on a table with a height of 90 cm. Control rats can efficiently navigate from the first point to the end point of the labyrinth. When an animal navigates through a maze spends more time in the pathways, it suggests the presence of unpleasant or anxiety-inducing conditions, which can be indicative of stress. In contrast, the decreased amount of time in the pathways indicates that these regions are safe or shelter enough. In our experiment, a square maze was built with food strategically placed on both the right and left edges to entice the mice. The mice were placed at the center of the maze, which was then assigned as the starting point. Furthermore, the mice were given unlimited time to explore the maze. Once the mice reached either the right or left side, that side was selected as the target point. Then, the opposite side was shut off, effectively trapping the mice for a duration of 30 seconds. During this time, the food on the other side was removed. Afterwards, the mice were returned to the original site, and the time it took for the mice to come back and eat food at the desired location was measured. The T-Maze Test was performed twice, both before and after the administration of the Centella asiatica and PEG formulation, with statistical significance later assessed using a paired t-test. 2.6 Brain Dissection of Mice Following the 14-day treatment period, mice were euthanized by cervical dislocation, and their brains were collected for analysis. The mice were anesthetized with 80 mg/kg of ketamine before dissection. A longitudinal incision was made along the central axis of the scalp, and the skull was carefully opened to reveal the brain. After extraction, the brain was preserved in formaldehyde for subsequent histological examination. 2.7 TNF-α Staning and Flowcytometry The brain organ was cleansed with NaCl and thereafter separated into its right and left hemispheres for the purpose of creating preparations and conducting TNF-α assay using Flow Cytometry. The brain organ was thereafter placed in a petri plate containing 2 mL of sterile PBS (Phosphate Buffer Saline). The organ was then pulverized using a syringe tip to extract brain cells. Next, the liquid portion was passed through a cell filter in order to eliminate any solid particles. Subsequently, a volume of 1 mL was extracted and put into a microtube for centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10°C. The liquid portion was removed and the solid portion was treated with 50 µL of BD Cytofix Fixation Buffer solution and left to sit in a cold storage container for 20 minutes. The goal of providing this buffer is to puncture the cells prior to being stained with antibodies. Following the incubation period, the cells underwent centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10°C. The liquid portion was removed and the solid portion was rinsed with 500 µL of WPS solution. It was then placed back in a cold storage container for 20 minutes and subsequently spun at a speed of 2500 rpm for 5 minutes at a temperature of 10°C. After removing the liquid portion, known as the supernatant, the remaining substance was subjected to antibody staining. This process involved applying 50 µL of TNF-α Ab to the solid material. The cell suspension was placed in a refrigerated container and kept at a low temperature for a duration of 20 minutes. Following the incubation period, the cells were subjected to centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10°C. The liquid portion above the sediment was thereafter discarded and the solid portion was rinsed with 500 µL of sterile PBS and transferred to Flow Tubes for Flow Cytometry and data analysis will be performed through ANOVA and t-test. 3. RESULT 3.1 GC-MS of Extraction Extraction process in Centella asiatica can change the structure and composition of its chemical compounds so the GC-MS test was chosen to identify active compounds in the Pegagan extract before the extract synthesis with pEG-400. The GC-MS test was chosen because of its ability to analyze without the need for a reference standard. [ 19 ],[ 20 ]. The results of the analysis identified several compounds that play a role in improving cognitive function, including tryptamine, gamma-sitosterol (γ-sitosterol), and beta-sitosterol (β-sitosterol) [ 21 ]. The data from Table 1 shows three compounds in the sample with different relative quantities and varying levels of identification reliability. The relative quantity of tryptamine has a relatively small area percentage, which is 1.79%, indicating that this compound is present in a smaller amount than the other two compounds. γ-sitosterol and β-sitosterol have the same area percentage, which is 7.22%, indicating that these two compounds are present in larger and relatively equal amounts in the sample that Tryptamine, Gamma and Beta-sitosterol contributes effects on the central nervous system. Table 1 Compounds found from GC-MS extraction Chemical Compounds Retention Time (RT) Area (%) Baseline Height (Ab) Absolute Height (Ab) Peak Width 50% (min) Quality Mol Weight (amu) Tryptamine 32.584 1.79 140135 459704 0.081 27 160.100 Gamma-Sitosterol 38.146 7.22 445099 1105685 104 99 414.386 Beta-Sitosterol 38.146 7.22 445099 1105685 104 91 414.386 3.2 Characterization of Extract Characterization was performed on DLS particle analyzer whereas the formulation of Centella extract at a 1:100 ratio with pEG-400 demonstrated superior performance compared to the other formulations as seen in Table 2 , as it resulted in nanoparticles with optimal size characteristics. pEG nanoparticles encapsulated extract exhibited enhanced properties, including a reduced size, improved biocompatibility, prolonged blood circulation time, and minimal toxicity and that have been harnessed to develop a novel delivery platform capable of efficiently transporting therapeutic agents to the blood-brain barrier [ 22 ]. Drug delivery across the BBB use NPs with diameters between 10–100 nm were proven to be effective [ 23 ]. Table 2 DLS Result Sample Refraction Index Dispersant Dispersant Index Temperature (oC) Minimum Particle size (nm) Maximum Particle size (nm) 1:1 1,59 Water 1,33 25 2.91 nm 4.14 nm 1:100 1,59 Water 1,33 25 20.27 nm 25.12 nm 100:1 1,59 Water 1,33 25 43.85 nm 110.36 nm 3.3 T-Maze Cognitive Test The results of the T-maze cognitive test (refer to Table 3 ) for mice reveal distinct differences between the pre-test and post-test performances across groups. In the control group, minor improvements are observed, such as a decrease in scores from 8.44 to 7.56 in U1 and from 7.37 to 6.37 in U5, which may indicate natural adaptation or placebo effects. In the 1:1 dose group, there are significant reductions in scores across most trials, with U1 decreasing from 10.56 to 8.34 and U3 from 8.59 to 6.49, suggesting this dosage has the strongest effect on cognitive improvement. The 1:100 dose group also shows a reduction in scores, such as U1 decreasing from 7.51 to 6.58 and U5 from 6.47 to 5.92, but the effects are less pronounced compared to the 1:1 dosage. Conversely, the 100:1 dose group exhibits mixed results, with U1 improving from 7.56 to 8.56, but U3 and U5 showing a decline, from 7.79 to 6.39 and 7.29 to 5.42, respectively, indicating inconsistent efficacy at this dosage. Overall, the 1:1 dose group appears to be the most effective treatment in improving cognitive performance, while the control group serves as a baseline for natural or placebo effects. The dose 1:1 and 1:100 groups show statistically significant improvements in cognitive performance, while the control and 100:1 groups do not demonstrate significant changes. This suggests that the treatments with a 1:1 and 1:100 ratio of the active agent are the more effective in improving cognitive function in the T-maze test refer to Tables 3 and 4 . Table 3 Pre test and post test result of T Maze Test Groups P1 P2 P3 P4 P5 Control Pre-Test 8.44 6.15 6.58 7.15 7.37 Post-Test 7.56 8.53 7.49 6.19 6.37 Dose 1:1 Pre-Test 10.56 8.34 8.59 7.1 7.29 Post-Test 8.34 7.23 6.49 6.32 5.29 Dose 1:100 Pre-Test 7.51 8.34 7.39 7.45 6.47 Post Test 6.58 7.34 6.59 6.9 5.92 Dose 100:1 Pre-Test 7.56 7.34 7.79 7.54 7.29 Post-Test 8.56 7.34 6.39 6.53 5.42 Note: P = number of experiment Table 4 Paired t-test result of T Maze Test Group t-Statistic p-Value Interpretation Control Group -0.133 0.901 No significant difference Dose 1:1 Group 5.635 0.005 Highly significant improvement Dose 1:100 Group 8.163 0.001 Highly significant improvement Dose 100:1 Group 1.271 0.273 No significant difference 3.4 Flow Cytometry Based on The 1:100 dose appears to be the most effective in reducing TNFα expression, as evidenced by its significantly lower fluorescence intensity distribution. In comparison, the 1:1 and 100:1 doses show some effect on TNFα expression, but they do not seem to be as effective as the 1:100 dose. The control group, which represents untreated samples, displays a slightly broader spread in fluorescence intensity, suggesting a baseline level of TNFα expression. Notably, if the majority of fluorescence intensity values for the 1:100 dose fall outside the M1 marker region, it would further support the conclusion that this dose is effective in suppressing neuroinflammation. Flow cytometry analysis on Fig. 3 . and proven statistically through ANOVA and t-test on Fig. 4 . revealed that the control group exhibited the highest mean relative TNF-α expression at 74.65%, serving as the baseline. In the 1:1 dose group, TNF-α expression decreased to 62.50%, suggesting a mild reduction due to treatment. The 1:100 dose group showed a further decline in TNF-α expression to 54.75%, indicating a more substantial impact of the extract. Meanwhile, the 100:1 dose group displayed slightly higher TNF-α expression at 59.91%, though still lower than the control, with greater variability in response (standard deviation of 8.03). Overall, all extract-treated groups exhibited dose-dependent reductions in TNF-α expression compared to the control, with the 1:100 dose group showing the most pronounced effect. The histograms and bar graphs visually confirmed these findings, with the 1:100 formulation demonstrating the most significant reduction in TNF-α expression (54.59%). 3.5 Histopathological Analysis of Mice Brain Tissues This histopathological image of the brain (Table 5 ) was obtained after treatment with lipopolysaccharide (LPS) for seven days to induce a neuroinflammatory response, primarily through increased TNF-α expression. LPS induction aims to mimic chronic inflammation conditions often associated with cognitive decline and neurodegenerative disorders. In the control group, the brain tissue structure appears relatively dense and well-organized, reflecting a baseline condition without treatment. Meanwhile, in the groups with 1:1 and 100:1 doses, there are indications of increased inflammation, as evidenced by structural changes in the tissue, and including reduced cell density and increase in apoptosis or necrosis as seen in Table 6 . In contrast, the group with a 1:100 dose exhibits a more organized tissue structure and higher cell density, indicating that this formulation is more effective in suppressing the inflammatory effects of LPS induction and protecting neural tissue from neuroinflammatory damage. Table 5. Histology imaging of Mice Brain Table 6 Apoptotic Cell Count in Histopathology Imaging of Mice Brain Tissue Cell Control P1 P2 P3 Control Pre-Test 55 6.15 6.58 7.15 Post-Test 62 8.53 7.49 6.19 Dose 1:1 Pre-Test 71 8.34 8.59 7.1 Post-Test 62,67 7.23 7.49 6.32 Dose 1:100 Pre-Test 7.51 8.34 7.39 7.45 Post Test 6.58 7.34 7.59 7.9 Dose 100:1 Pre-Test 7.56 7.34 7.79 7.54 Post-Test 8.56 7.34 6.39 6.53 DISCUSSION Centella asiatica constituents Centella asiatica contains various bioactive compounds, such as tryptamine, gamma sitosterol, and beta sitosterol, which contribute to neuroprotection and cognitive enhancement through multiple mechanisms. These compounds have been shown to inhibit acetylcholinesterase (AChE), increasing acetylcholine levels which are essential for memory and learning [ 24 ]. Additionally, beta sitosterol acts as a potential inhibitor of BACE1, an enzyme involved in Alzheimer’s disease, which plays a significant role in cognitive impairment [ 25 ]. In rat models that we performed, Centella asiatica treatment improves cognitive performance and provides neuroprotection through reduced apoptosis of neuron in histopathological analysis supported by a study that has proven Centella asiatica leaf ethanolic extract inhibits the reduction in hippocampal brain-derived neurotrophic factor (BDNF) concentration in the hippocampus in response to prolonged stress. Chronic stress increases the expression of proinflammatory cytokines like TNF-α in the hippocampus [ 26 ]. Neuroinflammation is a major factor in the progression of neurodegenerative diseases, driven largely by pro-inflammatory molecules like TNF-α (citation). Centella asiatica is known to reduce inflammation by lowering TNF-α levels [ 27 ],[ 28 ]. The 1:100 formulation is particularly effective at delivering Centella asiatica to the brain, helping to reduce inflammation in a targeted and sustained way. This reduction in inflammation is crucial for protecting neurons and slowing down cognitive decline, which is often exacerbated by chronic inflammation. PEG400 is a versatile polymer used in various biomedical and pharmaceutical contexts due to its unique physicochemical characteristics. PEG400 is used to stabilize nanoparticles and improve the delivery of therapeutic agents. By encapsulating within PEG400-based nanoparticles, it provides protection against degradation and enhances solubility [ 29 ]. How TNF alpha can affect the mice cognition TNF is largely produced within the central nervous system by myeloid-derived microglia, and in the case of neuroinflammation, by invading blood-borne immune cells [ 30 ]. Based on the results of flowcytometry, the dose of 1:100 formulation had the most significant effect on reducing TNF-α to 54.59%. But, the control group (k) exhibited the highest mean relative TNF-α expression among all groups, at 74.65%. The enhancement level of TNF-α has shown the capability to induce acute cognitive dysfunction and exaggerated sickness behavior with existing progressive neurodegeneration[ 4 ]. Excessive level of TNF-α disrupts the excitatory/inhibitory balance, leading to excitotoxicity. TNF-α is a neuromodulator cytokine that boosts excitatory/inhibitory ratio in postsynaptic neurons. High TNF-α levels can affect zinc levels, leading to decreased synaptic activity and cognitive impairment[ 31 ]. Additionally, TNF-α's inflammatory effects exacerbate mitochondrial impairment, further affecting cognitive abilities in aging mice [ 32 ]. ⁠Lower TNF-a concentrations lead to higher normal cell counts that correlate with improved cognitive performance and vice versa. Encapsulating Centella asiatica extract with pEG-400 is a critical step to enhance the stability and bioavailability of the active compound. On a molecular level, pEG-400 creates a hydrophilic layer around the nanoparticles, which prevents them from clumping together and ensures they stay evenly distributed in the body. Proper encapsulation of therapeutic agents, such as Centella asiatica, with polymers like pEG-400, plays a critical role in maintaining the stability and controlled release of active compounds [ 33 ]. This ensures that the drug acquired its optimal functional performance and bioavailability once it enters the body [ 33 ]. In the 1:1 formulation, there is not enough pEG-400 to fully encapsulate the extract, leading to irregular particle sizes. This inconsistency affects how well the drug can be absorbed and distributed, making the 1:1 formulation less effective as a medicine. Crossing the blood-brain barrier (BBB) represents a formidable challenge for drug delivery systems aimed at the central nervous system [ 34 ]. The dimension of nanoparticles is a critical determinant in this context. Excessively large nanoparticles might be prematurely removed by the immune system, preventing their entry into the brain. Likewise, overly small nanoparticles might degrade rapidly or fail to accumulate at the target site. The 1:100 formulation yields nanoparticles with dimensions approximately 5–7 nm, which is considered optimal for traversing the BBB. This size range facilitates efficient cellular uptake by neuronal cells, thereby ensuring effective delivery of the Centella asiatica extracts to their intended site of action. For an effective treatment, the active ingredient must be delivered in a consistent and reliable way. The 1:100 formulation is the most preferred, allowing a consistent and uniform amount of Centella asiatica extract in every dose. This consistency is vital to ensure that the drug works as intended each time. In contrast, the 1:1 formulation lacks uniformity, leading to unpredictable results, which is not ideal in a clinical setting. The even distribution and optimal size of nanoparticles found in the 1:100 formulation results in a tendency to deliver consistent therapeutic benefits. CONCLUSION The findings from this study underscore the promising neuroprotective potential of Centella asiatica extract when delivered via PEG nanoparticles, particularly in the context of TNF-α-induced cognitive decline. The optimized formulation at a 1:100 ratio demonstrated significant improvements in cognitive function, as evidenced by enhanced performance in T-maze tests, and a notable reduction in TNF-α expression by 54.59%. These results suggest that the encapsulation of Centella asiatica not only improves its bioavailability and stability but also enhances its efficacy as a therapeutic agent against cognitive impairments associated with neuroinflammation. The study contributes valuable insights into the role of natural compounds in neuroprotection and highlights the potential for further clinical investigations to develop effective treatments for age-related cognitive decline. Overall, this research supports the notion that targeting inflammatory pathways, such as TNF-α modulation, could be a viable strategy for preserving cognitive function and mitigating the effects of neurodegenerative diseases and with the potential for further exploration in human clinical trials in the future. Declarations DATA AVAILABILITY All data generated or analysed during this study are included in this published article and in supplementary files. CONFLICT OF INTEREST All authors declare that there is no conflict of interest regarding the publication of this research. AUTHOR CONTRIBUTION Author Contribution Nathania Nathania Conceptualization, design, data curation, formal analysis, investigation, methodology, project administration, resources, software, visualization, writing the original draft, review and editing Marson Putra Data curation, formal analysis, investigation, software, and writing the original draft Agoes Willyono Project administration, methodology, resources, supervision, validation, visualization Fransiskus Xaverius Data curation, formal analysis, investigation, software, and writing the original draft Asra Al Fauzi Project administration, methodology resources, supervision, validation, visualization, writing the original draft, review and editing RESEARCH FUNDING This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References World Health Organization: WHO. World failing to address dementia challenge. WHO Newsletters [Internet]. 2021 Sep 2; Available from: https://www.who.int/news/item/02-09-2021-world-failing-to-address-dementia-challenge Montine KS, Berson E, Phongpreecha T, Huang Z, Aghaeepour N, Zou JY, et al. Understanding the molecular basis of resilience to Alzheimer’s disease. Front Neurosci [Internet]. 2023 Dec 19;17. Available from: https://doi.org/10.3389/fnins.2023.1311157 Jiang H, Zhang X, Liu X, et al. The role of TNF-α in cognitive decline and neuroinflammation: Implications for Alzheimer's disease therapy. Front Immunol. 2022;13:10360935. doi: 10.3389/fimmu.2022.10360935 Hennessy E, Gormley S, Lopez-Rodriguez AB, Murray C, Murray C, Cunningham C. Systemic TNF-α produces acute cognitive dysfunction and exaggerated sickness behavior when superimposed upon progressive neurodegeneration. Brain Behav Immun. 2016 Sep 13;59:233–44. doi: 10.1016/j.bbi.2016.09.011 Da Rosa MM, De Amorim LC, De Oliveira Alves JV, Da Silva Aguiar IF, Da Silva Oliveira FG, Da Silva MV, et al. The promising role of natural products in Alzheimer’s disease. Brain Disord. 2022 Aug 17;7:100049. doi: 10.1016/j.dscb.2022.100049 Wright KM, McFerrin J, Magaña AA, Roberts J, Caruso M, Kretzschmar D, et al. Developing a Rational, Optimized Product of Centella asiatica for Examination in Clinical Trials: Real World Challenges. Front Nutr. 2022 Jan 14;8. Available from: https://doi.org/10.3389/fnut.2021.799137 Gray NE, Zweig JA, Caruso M, Martin MD, Zhu JY, Quinn JF, et al. Centella asiatica increases hippocampal synaptic density and improves memory and executive function in aged mice. Brain Behav. 2018 Jun 19;8(7). Available from: https://doi.org/10.1002/brb3.1024 Chen S, Yin ZJ, Jiang C, Ma ZQ, Fu Q, Qu R, et al. Asiaticoside attenuates memory impairment induced by transient cerebral ischemia–reperfusion in mice through anti-inflammatory mechanism. Pharmacol Biochem Behav. 2014 Mar 14;122:7–15. Available from: https://doi.org/10.1016/j.pbb.2014.03.004 Suk JS, Xu Q, Kim N, Hanes J, Ensign LM. PEGylation as a strategy for improving nanoparticle-based drug and gene delivery. Adv Drug Deliv Rev. 2016 Apr 1;99(Pt A):28-51. doi: 10.1016/j.addr.2015.09.012 Shi D, Beasock D, Fessler A, Szebeni J, Ljubimova JY, Afonin KA, et al. To PEGylate or not to PEGylate: Immunological properties of nanomedicine’s most popular component, polyethylene glycol and its alternatives. Adv Drug Deliv Rev. 2021 Dec 10;180:114079. Available from: https://doi.org/10.1016/j.addr.2021.114079 Santhanakrishnan KR, Koilpillai J, Narayanasamy D. PEGylation in Pharmaceutical Development: Current Status and Emerging Trends in Macromolecular and Immunotherapeutic Drugs. Cureus. 2024 Aug 12;16(8):e66669. doi: 10.7759/cureus.66669 Ermawati DE, Pamuji FA, Yugatama A. The effect of propylene glycol and polyethylene glycol 400 on physicochemical properties of peel off mask of nanosilver using bioreductor Sweet Orange Peels (Citrus sinensis L. Osbeck). Pharmaciana. 2021 Mar 31;11(1):121. Available from: https://doi.org/10.12928/pharmaciana.v11i1.19663 Caballero-Florán I, Cortés H, Borbolla-Jiménez FV, Florán-Hernández CD, Del Prado-Audelo ML, Magaña JJ, et al. PEG 400: Trehalose coating enhances Curcumin-Loaded PLGA nanoparticle internalization in neuronal cells. Pharmaceutics. 2023 May 25;15(6):1594. Available from: https://doi.org/10.3390/pharmaceutics15061594 Gamble JF, Tobyn M, Hamey R. Application of image-based particle size and shape characterization systems in the development of small molecule pharmaceuticals. J Pharm Sci. 2015 May;104(5):1563-74. doi: 10.1002/jps.24382 Deacon RMJ, Rawlins JNP. T-maze alternation in the rodent. Nat Protoc. 2006 Jun 1;1(1):7–12. Available from: https://doi.org/10.1038/nprot.2006.2 D’Isa R, Comi G, Leocani L. Apparatus design and behavioural testing protocol for the evaluation of spatial working memory in mice through the spontaneous alternation T-maze. Sci Rep. 2021 Oct 27;11(1). Available from: https://doi.org/10.1038/s41598-021-00402-7 Pioli EY, Gaskill BN, Gilmour G, Tricklebank MD, Dix SL, Bannerman D, et al. An automated maze task for assessing hippocampus-sensitive memory in mice. Behav Brain Res. 2013 Dec 12;261:249–57. Available from: https://doi.org/10.1016/j.bbr.2013.12.009 Wu CYC, Lerner FM, Silva ACE, Possoit HE, Hsieh TH, Neumann JT, et al. Utilizing the modified T-Maze to assess functional memory outcomes after cardiac arrest. J Vis Exp. 2018 Jan 5;(131). Available from: https://doi.org/10.3791/56694 Špadina M, Bohinc K, Zemb T, Dufrêche JF. Synergistic solvent extraction is driven by entropy. ACS Nano. 2019 Dec 24;13(12):13745-13758. doi: 10.1021/acsnano.9b07605 Wei X, Koo I, Kim S, Zhang X. Compound identification in GC-MS by simultaneously evaluating the mass spectrum and retention index. Analyst. 2014 Jan 1;139(10):2507–14. Available from: https://doi.org/10.1039/c3an02171h Ambavade SD, Misar AV, Ambavade PD. Pharmacological, nutritional, and analytical aspects of β-sitosterol: a review. Orient Pharm Exp Med. 2014 Apr 4;14(3):193–211. Available from: https://doi.org/10.1007/s13596-014-0151-9 Fakhoury M, Takechi R, Al-Salami H. Drug permeation across the blood-brain barrier: Applications of nanotechnology. Br J Med Med Res. 2015 Jan 7;6(6):547–56. Available from: https://doi.org/10.9734/bjmmr/2015/15493 Trickler WJ, Lantz SM, Murdock RC, Schrand AM, Robinson BL, Newport GD, et al. Silver nanoparticle induced blood-brain barrier inflammation and increased permeability in primary rat brain microvessel endothelial cells. Toxicol Sci. 2010 Aug 16;118(1):160–70. Available from: https://doi.org/10.1093/toxsci/kfq244 Firdaus Z, Gutti G, Ganeshpurkar A, Kumar A, Krishnamurthy S, Singh SK, et al. Centella asiatica improves memory and executive function in middle-aged rats by controlling oxidative stress and cholinergic transmission. J Ethnopharmacol. 2024 Feb 7;325:117888. Available from: https://doi.org/10.1016/j.jep.2024.117888 Mawaddani N, Wibowo NR, Nadhira QH, Pramifta RA. In silico study of Centella asiatica active compound as BACE1 inhibitor in Alzheimer’s disease. JSMARTech. 2020 Apr 27;1(2):36–40. Available from: https://doi.org/10.21776/ub.jsmartech.2020.001.02.3 Rochmah MA, Harini IM, Septyaningtrias DE, Sari DCR, Susilowati R. Centella Asiatica prevents increase of hippocampal tumor necrosis factor-α independently of its effect on brain-derived neurotrophic factor in rat model of chronic stress. Biomed Res Int. 2019 Mar 6;2019:1–7. Available from: https://doi.org/10.1155/2019/2649281 Lokanathan Y, Omar N, Puzi NNA, Saim A, Idrus RH. Recent updates in neuroprotective and neuroregenerative potential of Centella asiatica. PubMed. 2016 Jan 1;23(1):4–14. Available from: https://pubmed.ncbi.nlm.nih.gov/27540320 Orhan IE. Centella asiatica (L.) Urban: From traditional medicine to modern medicine with neuroprotective potential. Evid Based Complement Alternat Med. 2012 Jan 1;2012:1–8. Available from: https://doi.org/10.1155/2012/946259 Shang LY, Zhou MH, Cao SY, Zhang M, Wang PJ, Zhang S, et al. Effect of polyethylene glycol 400 on the pharmacokinetics and tissue distribution of baicalin by intravenous injection based on the enzyme activity of UGT1A8/1A9. Eur J Pharm Sci. 2022 Nov 12;180:106328. Available from: https://doi.org/10.1016/j.ejps.2022.106328 Mygind L, Bergh MSS, Tejsi V, Vaitheeswaran R, Lambertsen KL, Finsen B, et al. Tumor necrosis factor (TNF) is required for spatial learning and memory in male mice under physiological, but not immune-challenged conditions. Cells. 2021 Mar 9;10(3):608. Available from: https://doi.org/10.3390/cells10030608 Gonzalez Caldito N. Role of tumor necrosis factor-alpha in the central nervous system: a focus on autoimmune disorders. Front Immunol. 2023 Jul 7;14:1213448. doi: 10.3389/fimmu.2023.1213448 Xu X, Zhou B, Liu J, Ma Q, Zhang T, Wu X. RU360 alleviates postoperative cognitive dysfunction in aged mice by inhibiting MCU-mediated mitochondrial dysfunction. Neuropsychiatr Dis Treat. 2023 Jul 1;19:1531–42. Available from: https://doi.org/10.2147/ndt.s409568 Fernandes F, Dias-Teixeira M, Delerue-Matos C, Grosso C. Critical review of lipid-based nanoparticles as carriers of neuroprotective drugs and extracts. Nanomaterials (Basel). 2021 Feb 24;11(3):563. doi: 10.3390/nano11030563 Niazi SK. Non-invasive drug delivery across the blood-brain barrier: A prospective analysis. Pharmaceutics. 2023 Nov 7;15(11):2599. doi: 10.3390/pharmaceutics15112599. Additional Declarations No competing interests reported. Supplementary Files Supplementary1.docx Supplementary2Histopathology.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5987342","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":415064175,"identity":"25c0763a-e9a9-42aa-aa26-758c1f13ed88","order_by":0,"name":"Nathania Nathania","email":"","orcid":"","institution":"Faculty of Medicine, Universitas Airlangga","correspondingAuthor":false,"prefix":"","firstName":"Nathania","middleName":"","lastName":"Nathania","suffix":""},{"id":415064176,"identity":"10086f07-6c79-41d0-9d91-7d5408cb88de","order_by":1,"name":"Marson Putra","email":"","orcid":"","institution":"Iowa State University","correspondingAuthor":false,"prefix":"","firstName":"Marson","middleName":"","lastName":"Putra","suffix":""},{"id":415064177,"identity":"819408c8-d32c-49a6-abbd-11b4c75cccee","order_by":2,"name":"Agoes Willyono","email":"","orcid":"","institution":"National Hospital","correspondingAuthor":false,"prefix":"","firstName":"Agoes","middleName":"","lastName":"Willyono","suffix":""},{"id":415064178,"identity":"b2ee6ec2-2efa-4e1d-ab84-f24fa0490e47","order_by":3,"name":"Fransiskus Xaverius Rinaldi","email":"","orcid":"","institution":"Saint Antonius Jopu Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fransiskus","middleName":"Xaverius","lastName":"Rinaldi","suffix":""},{"id":415064179,"identity":"94109d19-badd-4756-bff5-13f96f0b9e96","order_by":4,"name":"Asra Al Fauzi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYPACCzn2HjBDAsJPYCOoRcKY5wypWhJ7zqAI4NHC33/24QeGGon0Hp4DbNIFNRb5DOyHHzA8KMNj/I10YwmGYxK5PbwNbNIzjklYNvCkGTAknMPjpBtsQPezSeTu52dgk+ZhkzBgYMhhYEhsw61D/vwx5h8M/yTSecBa/gG18L/Br8XgQBqbBGObRAIPyGG8bUAtEgRsMbyRxmaR2Cdh2MNzsNmat0/CgE3imcEBfH6RAzrsxodvNvI8PMkHb/N8qzPg509++PAHnhADgwQwydgApkAxcoCAhlEwCkbBKBgFBAAAxHM/ZKLxEu0AAAAASUVORK5CYII=","orcid":"","institution":"Universitas Airlangga Faculty of Medicine–Dr. Soetomo Academic General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Asra","middleName":"Al","lastName":"Fauzi","suffix":""}],"badges":[],"createdAt":"2025-02-08 11:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5987342/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5987342/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76191869,"identity":"d64f0c1b-50a7-4bba-bff5-7269d8f76344","added_by":"auto","created_at":"2025-02-13 09:47:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34768,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental Groups Illustration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental Groups Illustration of Mice consisting of control, and 3 dose groups (1:1, 1:100, 100:1)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/aa0b9e27d5c87035b1fa0e8c.png"},{"id":76191863,"identity":"f394b64d-6fd3-48fd-b8c3-18ae003d682f","added_by":"auto","created_at":"2025-02-13 09:47:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustration of T-Maze Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIllustration of the T-Maze Test conducted by the author to assess cognitive performance in mice, performed both before and after the administration of the extract.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/8a3d1dd1070ee3ba84434689.png"},{"id":76193033,"identity":"4e250fd7-ccf9-4918-9317-12290668ce64","added_by":"auto","created_at":"2025-02-13 09:55:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistogram of TNF alpha levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHistogram of TNF alpha levels (a) Dose 1:100, (b) dose 100:1, (c) dose 1:1, and (d) control group\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/a0a6189b558322de2bff37a2.png"},{"id":76191849,"identity":"6cbcc5de-cd96-4bdd-a894-9f5c7ce6d111","added_by":"auto","created_at":"2025-02-13 09:47:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTNF-a levels on Flowcytometry\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/96fbea85b47ca14705f06565.png"},{"id":84222112,"identity":"d21c2e1d-f02d-45e7-95e9-a1dc4949415c","added_by":"auto","created_at":"2025-06-09 12:01:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1325038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/240f9378-b37e-4830-82a8-75019e986271.pdf"},{"id":76191845,"identity":"60c9c164-8bd0-4e95-ad9d-b4e05db8dd13","added_by":"auto","created_at":"2025-02-13 09:47:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12055,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/03dbee3cc2d08e645d541322.docx"},{"id":76191880,"identity":"eca8a8ad-4ed3-4005-84bf-5cd6a9d5ba5f","added_by":"auto","created_at":"2025-02-13 09:47:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28233031,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2Histopathology.docx","url":"https://assets-eu.researchsquare.com/files/rs-5987342/v1/fdece4af1406746f50f7b09b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the Neuroprotective Mechanisms of Centella Asiatica Extract Delivered via pEG Nanoparticles in TNF-α-Induced Cognitive Decline: An In Vivo Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eCognitive decline in the elderly is a significant global health concern, affecting 65.6\u0026nbsp;million individuals worldwide and reducing quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e],[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Chronic inflammation in the brain, driven by pro-inflammatory cytokines like TNF-α (Tumor Necrosis Factor-alpha), plays a central role in cognitive decline and neurodegenerative diseases such as Alzheimer's disease. Elevated TNF-α levels activate microglia, the brain's immune cells, which release further inflammatory signals that can disrupt synaptic function, impair neuroplasticity, and promote neuronal damage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This inflammatory cascade contributes to the formation of amyloid beta plaques and tau tangles, hallmark features of Alzheimer's pathology. Additionally, TNF-α can compromise the blood-brain barrier, allowing harmful immune cells and molecules to infiltrate the brain. As a result, sustained neuroinflammation accelerates cognitive decline. Therapeutic strategies targeting TNF-α or modulating neuroinflammation are being explored as potential treatments to slow or halt the progression of neurodegenerative diseases, although challenges remain in delivering these therapies effectively to the brain [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, researchers have focused on the neuroprotective properties of natural compounds found in Centella asiatica extract, highlighting its potential in addressing neurodegenerative diseases. Secondary metabolites, such as tryptamine, an active metabolite of tryptophan have been linked to processes like cholinesterase inhibition and neuroprotection [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Centella asiatica has been recognized as a promising herbal remedy for cognitive impairment, supported by its pharmaceutical potential [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis validation is further strengthened by [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], who demonstrated that Centella asiatica improves the structure of the brain by enhancing dendritic branching in hippocampal neurons, a region critical for cognitive function. Additionally, asiaticoside, a key compound in Centella asiatica, has shown the ability to promote brain regeneration by reducing neuroinflammation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It achieves this through the suppression of proinflammatory cytokines, such as TNF-α, and modulation of brain-derived neurotrophic factor (BDNF), making it a compelling candidate for addressing age-related cognitive decline.\u003c/p\u003e \u003cp\u003eBy coating nanoparticles with PEG, a hydrophilic layer is created that keeps the particles from aggregating and stabilizes them in a variety of biological settings [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The drug's half-life in the body is prolonged by this stabilization, which also shields the nanoparticles from immune system identification and premature breakdown [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, PEG increases the solubility of hydrophobic medications, boosting their bioavailability and facilitating easier absorption [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, PEGylation gives nanoparticles \"stealth\" qualities that help them avoid detection by the immune system and stay in the bloodstream longer, increasing their capacity to target specific areas.\u003c/p\u003e \u003cp\u003eThis study demonstrates the efficacy of Centella asiatica encapsulated in PEG 400 nanoparticles in reducing TNF-alpha expression. The nanoparticle-based drug delivery system of pEG400 system improve drugs efficacy in central nervous system (CNS) by decreasing the density and viscosity of the solution, and improve the dispersibility of the particle [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Findings from recent study showed that PEG400-coated curcumin substantially decreases its toxicity and enhances its bioavailability. This improvement leads to increased antioxidant and anti-inflammatory activity, thereby providing greater therapeutic potential against neurological disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eAim\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study examines the cognitive advantages of a synthesized extract of \u003cem\u003eCentella asiatica\u003c/em\u003e that is coated with nanoparticles of PEG 400. This research attempts to clarify the potential neuroprotective effects of this new formulation by studying the connection between TNF-α levels and cognitive performance.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Ethical Clearance\u003c/h2\u003e \u003cp\u003eWe obtained ethical approval for our study from the Faculty of Medicine, Universitas Airlangga (Approval No. 38/EC/KEPK/FKUA/2024), in accordance with the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines. All experimental procedures were conducted following relevant animal welfare regulations and ethical considerations for research involving live vertebrates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Synthesis of Centella and pEG 400\u003c/h2\u003e \u003cp\u003eThe synthesis process of the CA extract formulation with PEG-400 involves dividing the formulation into three test groups (1:1, 1:100, 100:1). This comparison aims to optimize the variation in concentrations based on the proportion between the CA extract and PEG-400. The three test groups are as follows: group 1 (1 CA : 1 PEG-400); group 2 (1 CA : 100 PEG-400); and group 3 (100 CA : 1 PEG-400). By systematically analyzing these three ratios, the research aims to identify the most effective and stable formulation for therapeutic applications. These formulations are stirred using a magnetic stirrer for 15 minutes at a speed of 1,500 rpm and sonicated with a Scientz ultrasonicator for 5 minutes with 10-second pulse on and 5-second pulse off intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Particle Size Analyzer\u003c/h2\u003e \u003cp\u003eOnce we have collected samples from 3 different groups, we then proceed to evaluate their properties. Particle characterisation technologies have made significant advancements, providing a more profound understanding of powder systems and blends. Instruments capable of revealing the size and shape of particulate systems can offer much more detailed information about the particles being examined compared to traditional particle size measurements which utilize particle size analyzer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The PSA with BK-802N DLS nanoparticle size analyser biobase were conducted in Laboratory of Lembaga Ilmu Hayati, Teknik dan Rekayasa (LIHTR) Universitas Airlangga for each concentration (1:1; 1:100; 100:1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Animal Preparation\u003c/h2\u003e \u003cp\u003eBALB/c male mice (Mus musculus, approximately 35.08 g) were obtained from BBVF Pusvetma, Surabaya, Indonesia, and utilized for in vivo experiments. All experimental procedures were performed in accordance with approved protocols by the Faculty of Medicine, Universitas Airlangga Animal Care and Use Committee (IACUC). The study was carried out in compliance with the ARRIVE guidelines. The treatment group was divided into four subgroups, each containing five mice. These subgroups included a control group and three dose groups (Centella:pEG 400) with ratios of 1:1, 1:100, and 100:1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After a 7-day acclimatization period, all mice groups (except control) received lipopolysaccharide injection, in order to trigger neuroinflammation, leading to neuronal damage. After 7 days, each treatment group received a 500\u0026micro;L oral injection of the extract for the three dose groups over a period of 14 days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 T Maze Test\u003c/h2\u003e \u003cp\u003eThe T Maze Test is chosen as the assessment tool for cognitive performance in mice due to its lack of requirement for pre-training and its reliance exclusively on the inherent attraction of rodents to novelty and their spontaneous actions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The T Maze test (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e) for mice measures spatial and memory functions, helping to detect hippocampal dysfunction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Test model is performed in a plastic enclosure with 600 mm x 165 mm starting arm and 400 mm x 100 mm goal arms at the upper apex of the \"T\" [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The configuration is positioned on a table with a height of 90 cm. Control rats can efficiently navigate from the first point to the end point of the labyrinth. When an animal navigates through a maze spends more time in the pathways, it suggests the presence of unpleasant or anxiety-inducing conditions, which can be indicative of stress. In contrast, the decreased amount of time in the pathways indicates that these regions are safe or shelter enough. In our experiment, a square maze was built with food strategically placed on both the right and left edges to entice the mice. The mice were placed at the center of the maze, which was then assigned as the starting point. Furthermore, the mice were given unlimited time to explore the maze. Once the mice reached either the right or left side, that side was selected as the target point. Then, the opposite side was shut off, effectively trapping the mice for a duration of 30 seconds. During this time, the food on the other side was removed. Afterwards, the mice were returned to the original site, and the time it took for the mice to come back and eat food at the desired location was measured. The T-Maze Test was performed twice, both before and after the administration of the Centella asiatica and PEG formulation, with statistical significance later assessed using a paired t-test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Brain Dissection of Mice\u003c/h2\u003e \u003cp\u003eFollowing the 14-day treatment period, mice were euthanized by cervical dislocation, and their brains were collected for analysis. The mice were anesthetized with 80 mg/kg of ketamine before dissection. A longitudinal incision was made along the central axis of the scalp, and the skull was carefully opened to reveal the brain. After extraction, the brain was preserved in formaldehyde for subsequent histological examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 TNF-α Staning and Flowcytometry\u003c/h2\u003e \u003cp\u003eThe brain organ was cleansed with NaCl and thereafter separated into its right and left hemispheres for the purpose of creating preparations and conducting TNF-α assay using Flow Cytometry. The brain organ was thereafter placed in a petri plate containing 2 mL of sterile PBS (Phosphate Buffer Saline). The organ was then pulverized using a syringe tip to extract brain cells. Next, the liquid portion was passed through a cell filter in order to eliminate any solid particles. Subsequently, a volume of 1 mL was extracted and put into a microtube for centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10\u0026deg;C. The liquid portion was removed and the solid portion was treated with 50 \u0026micro;L of BD Cytofix Fixation Buffer solution and left to sit in a cold storage container for 20 minutes. The goal of providing this buffer is to puncture the cells prior to being stained with antibodies. Following the incubation period, the cells underwent centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10\u0026deg;C. The liquid portion was removed and the solid portion was rinsed with 500 \u0026micro;L of WPS solution. It was then placed back in a cold storage container for 20 minutes and subsequently spun at a speed of 2500 rpm for 5 minutes at a temperature of 10\u0026deg;C. After removing the liquid portion, known as the supernatant, the remaining substance was subjected to antibody staining. This process involved applying 50 \u0026micro;L of TNF-α Ab to the solid material. The cell suspension was placed in a refrigerated container and kept at a low temperature for a duration of 20 minutes. Following the incubation period, the cells were subjected to centrifugation at a speed of 2500 rpm for a duration of 5 minutes at a temperature of 10\u0026deg;C. The liquid portion above the sediment was thereafter discarded and the solid portion was rinsed with 500 \u0026micro;L of sterile PBS and transferred to Flow Tubes for Flow Cytometry and data analysis will be performed through ANOVA and t-test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULT","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 GC-MS of Extraction\u003c/h2\u003e \u003cp\u003eExtraction process in Centella asiatica can change the structure and composition of its chemical compounds so the GC-MS test was chosen to identify active compounds in the Pegagan extract before the extract synthesis with pEG-400. The GC-MS test was chosen because of its ability to analyze without the need for a reference standard. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e],[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results of the analysis identified several compounds that play a role in improving cognitive function, including tryptamine, gamma-sitosterol (γ-sitosterol), and beta-sitosterol (β-sitosterol) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The data from Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows three compounds in the sample with different relative quantities and varying levels of identification reliability. The relative quantity of tryptamine has a relatively small area percentage, which is 1.79%, indicating that this compound is present in a smaller amount than the other two compounds. γ-sitosterol and β-sitosterol have the same area percentage, which is 7.22%, indicating that these two compounds are present in larger and relatively equal amounts in the sample that Tryptamine, Gamma and Beta-sitosterol contributes effects on the central nervous system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCompounds found from GC-MS extraction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical Compounds\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetention Time (RT)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBaseline Height (Ab)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbsolute Height (Ab)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePeak Width 50% (min)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQuality\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMol Weight (amu)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTryptamine\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.584\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140135\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e459704\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e160.100\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGamma-Sitosterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.146\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1105685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e414.386\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Sitosterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.146\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e445099\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1105685\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e414.386\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characterization of Extract\u003c/h2\u003e \u003cp\u003eCharacterization was performed on DLS particle analyzer whereas the formulation of Centella extract at a 1:100 ratio with pEG-400 demonstrated superior performance compared to the other formulations as seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, as it resulted in nanoparticles with optimal size characteristics. pEG nanoparticles encapsulated extract exhibited enhanced properties, including a reduced size, improved biocompatibility, prolonged blood circulation time, and minimal toxicity and that have been harnessed to develop a novel delivery platform capable of efficiently transporting therapeutic agents to the blood-brain barrier [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Drug delivery across the BBB use NPs with diameters between 10–100 nm were proven to be effective [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDLS Result\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefraction Index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDispersant\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDispersant Index\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTemperature (oC)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum Particle size (nm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaximum Particle size (nm)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1:1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.91 nm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.14 nm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1:100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.27 nm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.12 nm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100:1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.85 nm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110.36 nm\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 T-Maze Cognitive Test\u003c/h2\u003e \u003cp\u003eThe results of the T-maze cognitive test (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) for mice reveal distinct differences between the pre-test and post-test performances across groups. In the control group, minor improvements are observed, such as a decrease in scores from 8.44 to 7.56 in U1 and from 7.37 to 6.37 in U5, which may indicate natural adaptation or placebo effects. In the 1:1 dose group, there are significant reductions in scores across most trials, with U1 decreasing from 10.56 to 8.34 and U3 from 8.59 to 6.49, suggesting this dosage has the strongest effect on cognitive improvement. The 1:100 dose group also shows a reduction in scores, such as U1 decreasing from 7.51 to 6.58 and U5 from 6.47 to 5.92, but the effects are less pronounced compared to the 1:1 dosage. Conversely, the 100:1 dose group exhibits mixed results, with U1 improving from 7.56 to 8.56, but U3 and U5 showing a decline, from 7.79 to 6.39 and 7.29 to 5.42, respectively, indicating inconsistent efficacy at this dosage. Overall, the 1:1 dose group appears to be the most effective treatment in improving cognitive performance, while the control group serves as a baseline for natural or placebo effects. The dose 1:1 and 1:100 groups show statistically significant improvements in cognitive performance, while the control and 100:1 groups do not demonstrate significant changes. This suggests that the treatments with a 1:1 and 1:100 ratio of the active agent are the more effective in improving cognitive function in the T-maze test refer to Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre test and post test result of T Maze Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eControl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.37\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDose 1:1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDose 1:100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDose 100:1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote: P = number of experiment\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePaired t-test result of T Maze Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo significant difference\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDose 1:1 Group\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.635\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly significant improvement\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDose 1:100 Group\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.163\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly significant improvement\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDose 100:1 Group\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.271\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo significant difference\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Flow Cytometry\u003c/h2\u003e \u003cp\u003eBased on The 1:100 dose appears to be the most effective in reducing TNFα expression, as evidenced by its significantly lower fluorescence intensity distribution. In comparison, the 1:1 and 100:1 doses show some effect on TNFα expression, but they do not seem to be as effective as the 1:100 dose. The control group, which represents untreated samples, displays a slightly broader spread in fluorescence intensity, suggesting a baseline level of TNFα expression. Notably, if the majority of fluorescence intensity values for the 1:100 dose fall outside the M1 marker region, it would further support the conclusion that this dose is effective in suppressing neuroinflammation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFlow cytometry analysis on Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e. and proven statistically through ANOVA and t-test on Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e. revealed that the control group exhibited the highest mean relative TNF-α expression at 74.65%, serving as the baseline. In the 1:1 dose group, TNF-α expression decreased to 62.50%, suggesting a mild reduction due to treatment. The 1:100 dose group showed a further decline in TNF-α expression to 54.75%, indicating a more substantial impact of the extract. Meanwhile, the 100:1 dose group displayed slightly higher TNF-α expression at 59.91%, though still lower than the control, with greater variability in response (standard deviation of 8.03). Overall, all extract-treated groups exhibited dose-dependent reductions in TNF-α expression compared to the control, with the 1:100 dose group showing the most pronounced effect. The histograms and bar graphs visually confirmed these findings, with the 1:100 formulation demonstrating the most significant reduction in TNF-α expression (54.59%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Histopathological Analysis of Mice Brain Tissues\u003c/h2\u003e \u003cp\u003eThis histopathological image of the brain (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) was obtained after treatment with lipopolysaccharide (LPS) for seven days to induce a neuroinflammatory response, primarily through increased TNF-α expression. LPS induction aims to mimic chronic inflammation conditions often associated with cognitive decline and neurodegenerative disorders. In the control group, the brain tissue structure appears relatively dense and well-organized, reflecting a baseline condition without treatment. Meanwhile, in the groups with 1:1 and 100:1 doses, there are indications of increased inflammation, as evidenced by structural changes in the tissue, and including reduced cell density and increase in apoptosis or necrosis as seen in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. In contrast, the group with a 1:100 dose exhibits a more organized tissue structure and higher cell density, indicating that this formulation is more effective in suppressing the inflammatory effects of LPS induction and protecting neural tissue from neuroinflammatory damage.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 5. Histology imaging of Mice Brain \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"496\" width=\"647\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eApoptotic Cell Count in Histopathology Imaging of Mice Brain Tissue\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDose 1:1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62,67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.32\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDose 1:100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDose 100:1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.54\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cb\u003eCentella asiatica constituents\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCentella asiatica contains various bioactive compounds, such as tryptamine, gamma sitosterol, and beta sitosterol, which contribute to neuroprotection and cognitive enhancement through multiple mechanisms. These compounds have been shown to inhibit acetylcholinesterase (AChE), increasing acetylcholine levels which are essential for memory and learning [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, beta sitosterol acts as a potential inhibitor of BACE1, an enzyme involved in Alzheimer’s disease, which plays a significant role in cognitive impairment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In rat models that we performed, Centella asiatica treatment improves cognitive performance and provides neuroprotection through reduced apoptosis of neuron in histopathological analysis supported by a study that has proven Centella asiatica leaf ethanolic extract inhibits the reduction in hippocampal brain-derived neurotrophic factor (BDNF) concentration in the hippocampus in response to prolonged stress. Chronic stress increases the expression of proinflammatory cytokines like TNF-α in the hippocampus [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNeuroinflammation is a major factor in the progression of neurodegenerative diseases, driven largely by pro-inflammatory molecules like TNF-α (citation). \u003cem\u003eCentella asiatica\u003c/em\u003e is known to reduce inflammation by lowering TNF-α levels [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e],[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The 1:100 formulation is particularly effective at delivering \u003cem\u003eCentella asiatica\u003c/em\u003e to the brain, helping to reduce inflammation in a targeted and sustained way. This reduction in inflammation is crucial for protecting neurons and slowing down cognitive decline, which is often exacerbated by chronic inflammation. PEG400 is a versatile polymer used in various biomedical and pharmaceutical contexts due to its unique physicochemical characteristics. PEG400 is used to stabilize nanoparticles and improve the delivery of therapeutic agents. By encapsulating within PEG400-based nanoparticles, it provides protection against degradation and enhances solubility [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e \u003cb\u003eHow TNF alpha can affect the mice cognition\u003c/b\u003e \u003c/p\u003e\u003cp\u003eTNF is largely produced within the central nervous system by myeloid-derived microglia, and in the case of neuroinflammation, by invading blood-borne immune cells [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on the results of flowcytometry, the dose of 1:100 formulation had the most significant effect on reducing TNF-α to 54.59%. But, the control group (k) exhibited the highest mean relative TNF-α expression among all groups, at 74.65%. The enhancement level of TNF-α has shown the capability to induce acute cognitive dysfunction and exaggerated sickness behavior with existing progressive neurodegeneration[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Excessive level of TNF-α disrupts the excitatory/inhibitory balance, leading to excitotoxicity. TNF-α is a neuromodulator cytokine that boosts excitatory/inhibitory ratio in postsynaptic neurons. High TNF-α levels can affect zinc levels, leading to decreased synaptic activity and cognitive impairment[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, TNF-α's inflammatory effects exacerbate mitochondrial impairment, further affecting cognitive abilities in aging mice [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. ⁠Lower TNF-a concentrations lead to higher normal cell counts that correlate with improved cognitive performance and vice versa.\u003c/p\u003e\u003cp\u003eEncapsulating \u003cem\u003eCentella asiatica\u003c/em\u003e extract with pEG-400 is a critical step to enhance the stability and bioavailability of the active compound. On a molecular level, pEG-400 creates a hydrophilic layer around the nanoparticles, which prevents them from clumping together and ensures they stay evenly distributed in the body. Proper encapsulation of therapeutic agents, such as Centella asiatica, with polymers like pEG-400, plays a critical role in maintaining the stability and controlled release of active compounds [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This ensures that the drug acquired its optimal functional performance and bioavailability once it enters the body [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the 1:1 formulation, there is not enough pEG-400 to fully encapsulate the extract, leading to irregular particle sizes. This inconsistency affects how well the drug can be absorbed and distributed, making the 1:1 formulation less effective as a medicine.\u003c/p\u003e\u003cp\u003eCrossing the blood-brain barrier (BBB) represents a formidable challenge for drug delivery systems aimed at the central nervous system [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The dimension of nanoparticles is a critical determinant in this context. Excessively large nanoparticles might be prematurely removed by the immune system, preventing their entry into the brain. Likewise, overly small nanoparticles might degrade rapidly or fail to accumulate at the target site. The 1:100 formulation yields nanoparticles with dimensions approximately 5–7 nm, which is considered optimal for traversing the BBB. This size range facilitates efficient cellular uptake by neuronal cells, thereby ensuring effective delivery of the Centella asiatica extracts to their intended site of action.\u003c/p\u003e\u003cp\u003eFor an effective treatment, the active ingredient must be delivered in a consistent and reliable way. The 1:100 formulation is the most preferred, allowing a consistent and uniform amount of \u003cem\u003eCentella asiatica\u003c/em\u003e extract in every dose. This consistency is vital to ensure that the drug works as intended each time. In contrast, the 1:1 formulation lacks uniformity, leading to unpredictable results, which is not ideal in a clinical setting. The even distribution and optimal size of nanoparticles found in the 1:100 formulation results in a tendency to deliver consistent therapeutic benefits.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings from this study underscore the promising neuroprotective potential of \u003cem\u003eCentella asiatica\u003c/em\u003e extract when delivered via PEG nanoparticles, particularly in the context of TNF-α-induced cognitive decline. The optimized formulation at a 1:100 ratio demonstrated significant improvements in cognitive function, as evidenced by enhanced performance in T-maze tests, and a notable reduction in TNF-α expression by 54.59%. These results suggest that the encapsulation of \u003cem\u003eCentella asiatica\u003c/em\u003e not only improves its bioavailability and stability but also enhances its efficacy as a therapeutic agent against cognitive impairments associated with neuroinflammation. The study contributes valuable insights into the role of natural compounds in neuroprotection and highlights the potential for further clinical investigations to develop effective treatments for age-related cognitive decline. Overall, this research supports the notion that targeting inflammatory pathways, such as TNF-α modulation, could be a viable strategy for preserving cognitive function and mitigating the effects of neurodegenerative diseases and with the potential for further exploration in human clinical trials in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and in supplementary files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that there is no conflict of interest regarding the publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eContribution\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eNathania Nathania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eConceptualization, design, data curation, formal analysis, investigation, methodology, project administration, resources, software, visualization, writing the original draft, review and editing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eMarson Putra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eData curation, formal analysis, investigation, software, and writing the original draft\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eAgoes Willyono\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eProject administration, methodology, resources, supervision, validation, visualization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eFransiskus Xaverius\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eData curation, formal analysis, investigation, software, and writing the original draft\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.9679%;\"\u003e\n \u003cp\u003eAsra Al Fauzi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.0321%;\"\u003e\n \u003cp\u003eProject administration, methodology resources, supervision, validation, visualization, writing the original draft, review and editing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRESEARCH FUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization: WHO. World failing to address dementia challenge. WHO Newsletters [Internet]. 2021 Sep 2; Available from: https://www.who.int/news/item/02-09-2021-world-failing-to-address-dementia-challenge\u003c/li\u003e\n\u003cli\u003eMontine KS, Berson E, Phongpreecha T, Huang Z, Aghaeepour N, Zou JY, et al. Understanding the molecular basis of resilience to Alzheimer\u0026rsquo;s disease. Front Neurosci [Internet]. 2023 Dec 19;17. Available from: https://doi.org/10.3389/fnins.2023.1311157\u003c/li\u003e\n\u003cli\u003eJiang H, Zhang X, Liu X, et al. The role of TNF-\u0026alpha; in cognitive decline and neuroinflammation: Implications for Alzheimer\u0026apos;s disease therapy. Front Immunol. 2022;13:10360935. doi: 10.3389/fimmu.2022.10360935\u003c/li\u003e\n\u003cli\u003eHennessy E, Gormley S, Lopez-Rodriguez AB, Murray C, Murray C, Cunningham C. Systemic TNF-\u0026alpha; produces acute cognitive dysfunction and exaggerated sickness behavior when superimposed upon progressive neurodegeneration. Brain Behav Immun. 2016 Sep 13;59:233\u0026ndash;44. doi: 10.1016/j.bbi.2016.09.011\u003c/li\u003e\n\u003cli\u003eDa Rosa MM, De Amorim LC, De Oliveira Alves JV, Da Silva Aguiar IF, Da Silva Oliveira FG, Da Silva MV, et al. The promising role of natural products in Alzheimer\u0026rsquo;s disease. Brain Disord. 2022 Aug 17;7:100049. doi: 10.1016/j.dscb.2022.100049\u003c/li\u003e\n\u003cli\u003eWright KM, McFerrin J, Maga\u0026ntilde;a AA, Roberts J, Caruso M, Kretzschmar D, et al. Developing a Rational, Optimized Product of Centella asiatica for Examination in Clinical Trials: Real World Challenges. Front Nutr. 2022 Jan 14;8. Available from: https://doi.org/10.3389/fnut.2021.799137\u003c/li\u003e\n\u003cli\u003eGray NE, Zweig JA, Caruso M, Martin MD, Zhu JY, Quinn JF, et al. Centella asiatica increases hippocampal synaptic density and improves memory and executive function in aged mice. Brain Behav. 2018 Jun 19;8(7). Available from: https://doi.org/10.1002/brb3.1024\u003c/li\u003e\n\u003cli\u003eChen S, Yin ZJ, Jiang C, Ma ZQ, Fu Q, Qu R, et al. Asiaticoside attenuates memory impairment induced by transient cerebral ischemia\u0026ndash;reperfusion in mice through anti-inflammatory mechanism. Pharmacol Biochem Behav. 2014 Mar 14;122:7\u0026ndash;15. Available from: https://doi.org/10.1016/j.pbb.2014.03.004\u003c/li\u003e\n\u003cli\u003eSuk JS, Xu Q, Kim N, Hanes J, Ensign LM. PEGylation as a strategy for improving nanoparticle-based drug and gene delivery. Adv Drug Deliv Rev. 2016 Apr 1;99(Pt A):28-51. doi: 10.1016/j.addr.2015.09.012\u003c/li\u003e\n\u003cli\u003eShi D, Beasock D, Fessler A, Szebeni J, Ljubimova JY, Afonin KA, et al. To PEGylate or not to PEGylate: Immunological properties of nanomedicine\u0026rsquo;s most popular component, polyethylene glycol and its alternatives. Adv Drug Deliv Rev. 2021 Dec 10;180:114079. Available from: https://doi.org/10.1016/j.addr.2021.114079\u003c/li\u003e\n\u003cli\u003eSanthanakrishnan KR, Koilpillai J, Narayanasamy D. PEGylation in Pharmaceutical Development: Current Status and Emerging Trends in Macromolecular and Immunotherapeutic Drugs. Cureus. 2024 Aug 12;16(8):e66669. doi: 10.7759/cureus.66669\u003c/li\u003e\n\u003cli\u003eErmawati DE, Pamuji FA, Yugatama A. The effect of propylene glycol and polyethylene glycol 400 on physicochemical properties of peel off mask of nanosilver using bioreductor Sweet Orange Peels (Citrus sinensis L. Osbeck). Pharmaciana. 2021 Mar 31;11(1):121. Available from: https://doi.org/10.12928/pharmaciana.v11i1.19663\u003c/li\u003e\n\u003cli\u003eCaballero-Flor\u0026aacute;n I, Cort\u0026eacute;s H, Borbolla-Jim\u0026eacute;nez FV, Flor\u0026aacute;n-Hern\u0026aacute;ndez CD, Del Prado-Audelo ML, Maga\u0026ntilde;a JJ, et al. PEG 400: Trehalose coating enhances Curcumin-Loaded PLGA nanoparticle internalization in neuronal cells. Pharmaceutics. 2023 May 25;15(6):1594. Available from: https://doi.org/10.3390/pharmaceutics15061594\u003c/li\u003e\n\u003cli\u003eGamble JF, Tobyn M, Hamey R. Application of image-based particle size and shape characterization systems in the development of small molecule pharmaceuticals. J Pharm Sci. 2015 May;104(5):1563-74. doi: 10.1002/jps.24382\u003c/li\u003e\n\u003cli\u003eDeacon RMJ, Rawlins JNP. T-maze alternation in the rodent. Nat Protoc. 2006 Jun 1;1(1):7\u0026ndash;12. Available from: https://doi.org/10.1038/nprot.2006.2\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Isa R, Comi G, Leocani L. Apparatus design and behavioural testing protocol for the evaluation of spatial working memory in mice through the spontaneous alternation T-maze. Sci Rep. 2021 Oct 27;11(1). Available from: https://doi.org/10.1038/s41598-021-00402-7\u003c/li\u003e\n\u003cli\u003ePioli EY, Gaskill BN, Gilmour G, Tricklebank MD, Dix SL, Bannerman D, et al. An automated maze task for assessing hippocampus-sensitive memory in mice. Behav Brain Res. 2013 Dec 12;261:249\u0026ndash;57. Available from: https://doi.org/10.1016/j.bbr.2013.12.009\u003c/li\u003e\n\u003cli\u003eWu CYC, Lerner FM, Silva ACE, Possoit HE, Hsieh TH, Neumann JT, et al. Utilizing the modified T-Maze to assess functional memory outcomes after cardiac arrest. J Vis Exp. 2018 Jan 5;(131). Available from: https://doi.org/10.3791/56694\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;padina M, Bohinc K, Zemb T, Dufr\u0026ecirc;che JF. Synergistic solvent extraction is driven by entropy. ACS Nano. 2019 Dec 24;13(12):13745-13758. doi: 10.1021/acsnano.9b07605\u003c/li\u003e\n\u003cli\u003eWei X, Koo I, Kim S, Zhang X. Compound identification in GC-MS by simultaneously evaluating the mass spectrum and retention index. Analyst. 2014 Jan 1;139(10):2507\u0026ndash;14. Available from: https://doi.org/10.1039/c3an02171h\u003c/li\u003e\n\u003cli\u003eAmbavade SD, Misar AV, Ambavade PD. Pharmacological, nutritional, and analytical aspects of \u0026beta;-sitosterol: a review. Orient Pharm Exp Med. 2014 Apr 4;14(3):193\u0026ndash;211. Available from: https://doi.org/10.1007/s13596-014-0151-9\u003c/li\u003e\n\u003cli\u003eFakhoury M, Takechi R, Al-Salami H. Drug permeation across the blood-brain barrier: Applications of nanotechnology. Br J Med Med Res. 2015 Jan 7;6(6):547\u0026ndash;56. Available from: https://doi.org/10.9734/bjmmr/2015/15493\u003c/li\u003e\n\u003cli\u003eTrickler WJ, Lantz SM, Murdock RC, Schrand AM, Robinson BL, Newport GD, et al. Silver nanoparticle induced blood-brain barrier inflammation and increased permeability in primary rat brain microvessel endothelial cells. Toxicol Sci. 2010 Aug 16;118(1):160\u0026ndash;70. Available from: https://doi.org/10.1093/toxsci/kfq244\u003c/li\u003e\n\u003cli\u003eFirdaus Z, Gutti G, Ganeshpurkar A, Kumar A, Krishnamurthy S, Singh SK, et al. Centella asiatica improves memory and executive function in middle-aged rats by controlling oxidative stress and cholinergic transmission. J Ethnopharmacol. 2024 Feb 7;325:117888. Available from: https://doi.org/10.1016/j.jep.2024.117888\u003c/li\u003e\n\u003cli\u003eMawaddani N, Wibowo NR, Nadhira QH, Pramifta RA. In silico study of Centella asiatica active compound as BACE1 inhibitor in Alzheimer\u0026rsquo;s disease. JSMARTech. 2020 Apr 27;1(2):36\u0026ndash;40. Available from: https://doi.org/10.21776/ub.jsmartech.2020.001.02.3\u003c/li\u003e\n\u003cli\u003eRochmah MA, Harini IM, Septyaningtrias DE, Sari DCR, Susilowati R. Centella Asiatica prevents increase of hippocampal tumor necrosis factor-\u0026alpha; independently of its effect on brain-derived neurotrophic factor in rat model of chronic stress. Biomed Res Int. 2019 Mar 6;2019:1\u0026ndash;7. Available from: https://doi.org/10.1155/2019/2649281\u003c/li\u003e\n\u003cli\u003eLokanathan Y, Omar N, Puzi NNA, Saim A, Idrus RH. Recent updates in neuroprotective and neuroregenerative potential of Centella asiatica. PubMed. 2016 Jan 1;23(1):4\u0026ndash;14. Available from: https://pubmed.ncbi.nlm.nih.gov/27540320\u003c/li\u003e\n\u003cli\u003eOrhan IE. Centella asiatica (L.) Urban: From traditional medicine to modern medicine with neuroprotective potential. Evid Based Complement Alternat Med. 2012 Jan 1;2012:1\u0026ndash;8. Available from: https://doi.org/10.1155/2012/946259\u003c/li\u003e\n\u003cli\u003eShang LY, Zhou MH, Cao SY, Zhang M, Wang PJ, Zhang S, et al. Effect of polyethylene glycol 400 on the pharmacokinetics and tissue distribution of baicalin by intravenous injection based on the enzyme activity of UGT1A8/1A9. Eur J Pharm Sci. 2022 Nov 12;180:106328. Available from: https://doi.org/10.1016/j.ejps.2022.106328\u003c/li\u003e\n\u003cli\u003eMygind L, Bergh MSS, Tejsi V, Vaitheeswaran R, Lambertsen KL, Finsen B, et al. Tumor necrosis factor (TNF) is required for spatial learning and memory in male mice under physiological, but not immune-challenged conditions. Cells. 2021 Mar 9;10(3):608. Available from: https://doi.org/10.3390/cells10030608\u003c/li\u003e\n\u003cli\u003eGonzalez Caldito N. Role of tumor necrosis factor-alpha in the central nervous system: a focus on autoimmune disorders. Front Immunol. 2023 Jul 7;14:1213448. doi: 10.3389/fimmu.2023.1213448\u003c/li\u003e\n\u003cli\u003eXu X, Zhou B, Liu J, Ma Q, Zhang T, Wu X. RU360 alleviates postoperative cognitive dysfunction in aged mice by inhibiting MCU-mediated mitochondrial dysfunction. Neuropsychiatr Dis Treat. 2023 Jul 1;19:1531\u0026ndash;42. Available from: https://doi.org/10.2147/ndt.s409568\u003c/li\u003e\n\u003cli\u003eFernandes F, Dias-Teixeira M, Delerue-Matos C, Grosso C. Critical review of lipid-based nanoparticles as carriers of neuroprotective drugs and extracts. Nanomaterials (Basel). 2021 Feb 24;11(3):563. doi: 10.3390/nano11030563\u003c/li\u003e\n\u003cli\u003eNiazi SK. Non-invasive drug delivery across the blood-brain barrier: A prospective analysis. Pharmaceutics. 2023 Nov 7;15(11):2599. doi: 10.3390/pharmaceutics15112599.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive impairment, Nanoparticle, in vivo, centella asiatica, herbal, neurodegenerative","lastPublishedDoi":"10.21203/rs.3.rs-5987342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5987342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eCognitive decline in the elderly is a pressing global health concern, affecting 65.6\u0026nbsp;million individuals worldwide and significantly diminishing quality of life. Elevated inflammatory markers, particularly Tumor Necrosis Factor-alpha (TNF-α), are strongly associated with neuronal damage and cognitive deterioration. Emerging evidence highlights the neuroprotective potential of natural compounds, such as those in \u003cem\u003eCentella asiatica\u003c/em\u003e extract, known for its pharmaceutical benefits in addressing neurodegenerative diseases.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e \u003cp\u003eThe research aims to evaluate the formulation\u0026rsquo;s impact on TNF-α expression and cognitive performance, focusing on its neuroprotective properties. By exploring this novel approach, the study seeks to contribute valuable insights into managing age-related cognitive decline, emphasizing TNF-α regulation as a potential therapeutic target.\u003c/p\u003e\u003ch2\u003eResults and Discussion\u003c/h2\u003e \u003cp\u003eThe GC-MS analysis identified active compounds in \u003cem\u003eCentella asiatica\u003c/em\u003e extract, including tryptamine (1.79%), γ-sitosterol, and β-sitosterol (each 7.22%), essential for cognitive improvement. These findings confirm the presence of neuroprotective agents prior to synthesis with PEG-400. On PSA, the 1:100 extract-to-PEG-400 ratio produced ideal nanoparticles (20\u0026ndash;25 nm) optimal for blood-brain barrier (BBB) penetration. Smaller nanoparticles in this range demonstrate effective therapeutic delivery, minimizing immune clearance and degradation. T-maze tests revealed significant cognitive improvements in the 1:1 and 1:100 groups, with the latter showing the most consistent results. Flow cytometry indicated a dose-dependent reduction in TNF-α expression, with the 1:100 group achieving the greatest decrease (54.59%) compared to the control (74.65%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe 1:100 (Centella:pEG) formulation demonstrated superior stability, bioavailability, and efficacy due to optimized nanoparticle size and consistent compound delivery, supporting \u003cem\u003eCentella asiatica\u003c/em\u003e encapsulation as a promising neurotherapeutic strategy.\u003c/p\u003e","manuscriptTitle":"Unveiling the Neuroprotective Mechanisms of Centella Asiatica Extract Delivered via pEG Nanoparticles in TNF-α-Induced Cognitive Decline: An In Vivo Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 09:47:13","doi":"10.21203/rs.3.rs-5987342/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdda57b0-ba38-4303-be04-c88fd27eeda8","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44259739,"name":"Biological sciences/Neuroscience"},{"id":44259740,"name":"Biological sciences/Plant sciences"},{"id":44259741,"name":"Health sciences/Molecular medicine"},{"id":44259742,"name":"Health sciences/Neurology"},{"id":44259743,"name":"Physical sciences/Nanoscience and technology"}],"tags":[],"updatedAt":"2025-06-09T11:53:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 09:47:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5987342","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5987342","identity":"rs-5987342","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}